Model Deployment : Machine Learning Model Experiment Logging and Tracking Using Open-Source Frameworks¶
1. Table of Contents ¶
This project explores open-source solutions for managing the end-to-end lifecycle of machine learning models, focusing on the use of MLflow for experiment tracking, model selection, and production deployment in Python. The objective was to design a reproducible workflow for evaluating multiple ensemble classifiers, selecting the optimal candidate based on validation and cross-validation metrics, and operationalizing the chosen model for production use. The project workflow involved several stages: first, hyperparameter tuning experiments were conducted across multiple ensemble model types, with all runs logged and organized in the MLflow Tracking UI. Next, optimal candidates were identified for each model family by programmatically querying run metrics, and the best overall model was selected based on a multi-metric ranking strategy. This model was then registered in the MLflow Model Registry, ensuring version-controlled storage alongside associated artifacts and metadata. To enable stable and maintainable production usage, the registered model was assigned a symbolic alias allowing seamless loading of the production model without hardcoding version numbers and simplifying future updates. The selected model was then evaluated on an independent test set to confirm generalization performance before being loaded for production inference via the MLflow PyFunc interface. The entire process—from experiment tracking to production deployment—was automated through the MLflow Python API, demonstrating how open-source MLOps tooling can streamline model governance, reproducibility, and deployment. All results were consolidated in a Summary presented at the end of the document.
Machine Learning Model Lifecycle Management is the systematic process of overseeing a model’s journey from initial development through production deployment, monitoring, and eventual retirement. In the context of MLOps, it encompasses every stage of a model’s evolution — including data preparation, model training, evaluation, version control, deployment, monitoring, retraining, and governance. The goal is to ensure that models remain accurate, reliable, and aligned with evolving business needs over time. Effective lifecycle management addresses challenges such as model drift, reproducibility, compliance, and scalability, while integrating automation to reduce operational overhead. It promotes collaboration between data scientists, engineers, and operations teams by using standardized processes and tooling to ensure smooth transitions between research and production environments. In modern MLOps practices, lifecycle management is supported by platforms and frameworks that provide tracking, registry, deployment orchestration, and monitoring capabilities, enabling continuous delivery and improvement of AI solutions.
MLflow is an open-source platform designed to manage the complete lifecycle of machine learning models, making it a central component in MLOps workflows. It provides four key capabilities: tracking experiments to log parameters, metrics, and artifacts; projects to package and share reproducible ML code; model registry to store, version, and annotate models; and model serving to deploy them in production environments. By offering a unified interface for logging and retrieving experiment data, MLflow helps teams compare results, trace model evolution, and enforce reproducibility. Its Model Registry acts as a centralized hub for managing versions, promoting models to production, and assigning aliases for easy referencing. MLflow supports multiple model formats — including Python functions, scikit-learn models, and deep learning frameworks — and integrates with popular deployment targets. In machine learning lifecycle management, MLflow bridges the gap between experimentation and production, providing transparency, governance, and automation across all stages.
Run Comparison in machine learning model lifecycle management refers to the process of evaluating and contrasting multiple training runs to identify the best-performing model for deployment. This involves analyzing key performance metrics, hyperparameters, and resource usage across different experiments. In MLOps, run comparison ensures that selection decisions are data-driven, reproducible, and aligned with business objectives. Within MLflow, run comparison is facilitated by both the Tracking UI and the Search API. The UI provides sortable tables and visualizations for side-by-side metric analysis, while the Search API allows programmatic filtering and ranking of runs using multiple criteria. This enables teams to systematically evaluate candidate models, pinpoint optimal configurations, and maintain an auditable record of decision-making. By combining interactive visual review with automated selection pipelines, MLflow’s run comparison capabilities streamline the transition from experimentation to registry.
Artifact Storage refers to the practice of preserving all files and outputs generated during the machine learning process, ensuring reproducibility and traceability. Artifacts can include trained model binaries, datasets, configuration files, visualizations, and evaluation reports. In the context of MLOps, centralized artifact storage ensures that every stage of model development is documented and recoverable, enabling consistent retraining, auditing, and compliance verification. In MLflow, artifact storage is integrated into the tracking system: every logged run can store artifacts either locally or on remote storage backends such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. MLflow automatically organizes artifacts by experiment and run ID, allowing quick retrieval for analysis, deployment, or comparison. This eliminates the risk of “lost” models or mismatched dependencies, making artifact storage a foundational pillar of reliable lifecycle management.
Model Registry in machine learning lifecycle management serves as a centralized repository for storing, versioning, and managing machine learning models throughout their lifespan. It enables teams to track which models are in development, testing, or production, ensuring controlled and auditable promotion between stages. In MLOps, a model registry supports governance by maintaining metadata such as version numbers, creation dates, and performance metrics. MLflow’s Model Registry extends this concept by allowing users to register models directly from experiment runs, associate them with descriptions and tags, and manage aliases for flexible referencing. Instead of relying solely on numeric versions or deprecated stage labels, aliases like prod or staging allow CI/CD pipelines to dynamically pull the correct model without manual intervention. This makes the MLflow Model Registry a cornerstone for production-grade MLOps, ensuring both stability and agility in model deployment workflows.
Model Inference is the process of using a trained machine learning model to generate predictions on new, unseen data. In the context of lifecycle management, inference bridges the gap between model development and real-world application, enabling AI systems to deliver value in production environments. Inference workflows must account for input data preprocessing, model loading, execution efficiency, and output formatting. In MLflow, model inference is supported through its PyFunc interface, which provides a standardized API for loading and executing models regardless of their underlying framework. Models can be loaded by URI from local files, remote storage, or directly from the MLflow Model Registry using version numbers or aliases. This abstraction simplifies integration into applications and services, ensuring consistent and scalable predictions across environments. By standardizing inference, MLflow reduces friction in deployment and makes model serving more maintainable.
Model Deployment, in the context of lifecycle management, is the process of making a trained machine learning model available in a production environment where it can serve predictions to end-users or downstream systems. Deployment strategies vary, from batch processing pipelines to real-time APIs, and must consider scalability, latency, monitoring, and security. In MLOps, deployment is not a one-time event — it is part of a continuous delivery process that supports model updates, rollback capabilities, and ongoing performance monitoring. MLflow facilitates deployment through its support for multiple serving options: models can be served locally for testing, deployed as REST APIs using MLflow’s built-in serving tools, or integrated with platforms like Kubernetes, AWS SageMaker, or Azure ML. By coupling deployment with the Model Registry and aliases, MLflow enables automated CI/CD pipelines to update production models without manual configuration changes. This approach ensures rapid, reliable, and traceable promotion of models from experimentation to production use.
1.1. Data Background ¶
An open Thyroid Disease Dataset from Kaggle (with all credits attributed to Jai Naru and Abuchi Onwuegbusi) was used for the analysis as consolidated from the following primary sources:
- Reference Repository entitled Differentiated Thyroid Cancer Recurrence from UC Irvine Machine Learning Repository
- Research Paper entitled Machine Learning for Risk Stratification of Thyroid Cancer Patients: a 15-year Cohort Study from the European Archives of Oto-Rhino-Laryngology
This study hypothesized that the various clinicopathological characteristics influence differentiated thyroid cancer recurrence between patients.
The dichotomous categorical variable for the study is:
- Recurred - Status of the patient (Yes, Recurrence of differentiated thyroid cancer | No, No recurrence of differentiated thyroid cancer)
The predictor variables for the study are:
- Age - Patient's age (Years)
- Gender - Patient's sex (M | F)
- Smoking - Indication of smoking (Yes | No)
- Hx Smoking - Indication of smoking history (Yes | No)
- Hx Radiotherapy - Indication of radiotherapy history for any condition (Yes | No)
- Thyroid Function - Status of thyroid function (Clinical Hyperthyroidism, Hypothyroidism | Subclinical Hyperthyroidism, Hypothyroidism | Euthyroid)
- Physical Examination - Findings from physical examination including palpation of the thyroid gland and surrounding structures (Normal | Diffuse Goiter | Multinodular Goiter | Single Nodular Goiter Left, Right)
- Adenopathy - Indication of enlarged lymph nodes in the neck region (No | Right | Extensive | Left | Bilateral | Posterior)
- Pathology - Specific thyroid cancer type as determined by pathology examination of biopsy samples (Follicular | Hurthel Cell | Micropapillary | Papillary)
- Focality - Indication if the cancer is limited to one location or present in multiple locations (Uni-Focal | Multi-Focal)
- Risk - Risk category of the cancer based on various factors, such as tumor size, extent of spread, and histological type (Low | Intermediate | High)
- T - Tumor classification based on its size and extent of invasion into nearby structures (T1a | T1b | T2 | T3a | T3b | T4a | T4b)
- N - Nodal classification indicating the involvement of lymph nodes (N0 | N1a | N1b)
- M - Metastasis classification indicating the presence or absence of distant metastases (M0 | M1)
- Stage - Overall stage of the cancer, typically determined by combining T, N, and M classifications (I | II | III | IVa | IVb)
- Response - Cancer's response to treatment (Biochemical Incomplete | Indeterminate | Excellent | Structural Incomplete)
1.2. Data Description ¶
- The initial tabular dataset was comprised of 383 observations and 17 variables (including 1 target and 16 predictors).
- 383 rows (observations)
- 17 columns (variables)
- 1/17 target (categorical)
- Recurred
- 1/17 predictor (numeric)
- Age
- 16/17 predictor (categorical)
- Gender
- Smoking
- Hx_Smoking
- Hx_Radiotherapy
- Thyroid_Function
- Physical_Examination
- Adenopathy
- Pathology
- Focality
- Risk
- T
- N
- M
- Stage
- Response
- 1/17 target (categorical)
In [1]:
##################################
# Loading Python Libraries
##################################
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import re
import pickle
%matplotlib inline
import mlflow
from mlflow.tracking import MlflowClient
from mlflow.models.signature import infer_signature
from mlflow.exceptions import RestException
import hashlib
import json
from urllib.parse import urlparse
import logging
from operator import truediv
from sklearn.preprocessing import OrdinalEncoder
from scipy import stats
from scipy.stats import pointbiserialr, chi2_contingency
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report
from sklearn.model_selection import train_test_split, ParameterGrid, StratifiedShuffleSplit
from sklearn.base import clone
In [2]:
##################################
# Defining file paths
##################################
DATASETS_ORIGINAL_PATH = r"datasets\original"
DATASETS_FINAL_PATH = r"datasets\final\complete"
DATASETS_FINAL_TRAIN_PATH = r"datasets\final\train"
DATASETS_FINAL_TRAIN_FEATURES_PATH = r"datasets\final\train\features"
DATASETS_FINAL_TRAIN_TARGET_PATH = r"datasets\final\train\target"
DATASETS_FINAL_VALIDATION_PATH = r"datasets\final\validation"
DATASETS_FINAL_VALIDATION_FEATURES_PATH = r"datasets\final\validation\features"
DATASETS_FINAL_VALIDATION_TARGET_PATH = r"datasets\final\validation\target"
DATASETS_FINAL_TEST_PATH = r"datasets\final\test"
DATASETS_FINAL_TEST_FEATURES_PATH = r"datasets\final\test\features"
DATASETS_FINAL_TEST_TARGET_PATH = r"datasets\final\test\target"
DATASETS_PREPROCESSED_PATH = r"datasets\preprocessed"
DATASETS_PREPROCESSED_TRAIN_PATH = r"datasets\preprocessed\train"
DATASETS_PREPROCESSED_TRAIN_FEATURES_PATH = r"datasets\preprocessed\train\features"
DATASETS_PREPROCESSED_TRAIN_TARGET_PATH = r"datasets\preprocessed\train\target"
DATASETS_PREPROCESSED_VALIDATION_PATH = r"datasets\preprocessed\validation"
DATASETS_PREPROCESSED_VALIDATION_FEATURES_PATH = r"datasets\preprocessed\validation\features"
DATASETS_PREPROCESSED_VALIDATION_TARGET_PATH = r"datasets\preprocessed\validation\target"
DATASETS_PREPROCESSED_TEST_PATH = r"datasets\preprocessed\test"
DATASETS_PREPROCESSED_TEST_FEATURES_PATH = r"datasets\preprocessed\test\features"
DATASETS_PREPROCESSED_TEST_TARGET_PATH = r"datasets\preprocessed\test\target"
In [3]:
##################################
# Loading the dataset
# from the DATASETS_ORIGINAL_PATH
##################################
thyroid_cancer = pd.read_csv(os.path.join("..", DATASETS_ORIGINAL_PATH, "Thyroid_Diff.csv"))
In [4]:
##################################
# Performing a general exploration of the dataset
##################################
print('Dataset Dimensions: ')
display(thyroid_cancer.shape)
Dataset Dimensions:
(383, 17)
In [5]:
##################################
# Listing the column names and data types
##################################
print('Column Names and Data Types:')
display(thyroid_cancer.dtypes)
Column Names and Data Types:
Age int64 Gender object Smoking object Hx Smoking object Hx Radiotherapy object Thyroid Function object Physical Examination object Adenopathy object Pathology object Focality object Risk object T object N object M object Stage object Response object Recurred object dtype: object
In [6]:
##################################
# Converting integer columns to float
# for a more convenient downstream processing
##################################
thyroid_cancer = thyroid_cancer.astype({
col: 'float64' for col in thyroid_cancer.select_dtypes(include='int').columns
})
print('Column Names and Data Types:')
display(thyroid_cancer.dtypes)
Column Names and Data Types:
Age float64 Gender object Smoking object Hx Smoking object Hx Radiotherapy object Thyroid Function object Physical Examination object Adenopathy object Pathology object Focality object Risk object T object N object M object Stage object Response object Recurred object dtype: object
In [7]:
##################################
# Renaming and standardizing the column names
# to replace blanks with undercores
##################################
thyroid_cancer.columns = thyroid_cancer.columns.str.replace(" ", "_")
In [8]:
##################################
# Taking a snapshot of the dataset
##################################
thyroid_cancer.head()
Out[8]:
| Age | Gender | Smoking | Hx_Smoking | Hx_Radiotherapy | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | M | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 27.0 | F | No | No | No | Euthyroid | Single nodular goiter-left | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Indeterminate | No |
| 1 | 34.0 | F | No | Yes | No | Euthyroid | Multinodular goiter | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 2 | 30.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 3 | 62.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 4 | 62.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Micropapillary | Multi-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
In [9]:
##################################
# Selecting categorical columns (both object and categorical types)
# and listing the unique categorical levels
##################################
cat_cols = thyroid_cancer.select_dtypes(include=["object", "category"]).columns
for col in cat_cols:
print(f"Categorical | Object Column: {col}")
print(thyroid_cancer[col].unique())
print("-" * 40)
Categorical | Object Column: Gender ['F' 'M'] ---------------------------------------- Categorical | Object Column: Smoking ['No' 'Yes'] ---------------------------------------- Categorical | Object Column: Hx_Smoking ['No' 'Yes'] ---------------------------------------- Categorical | Object Column: Hx_Radiotherapy ['No' 'Yes'] ---------------------------------------- Categorical | Object Column: Thyroid_Function ['Euthyroid' 'Clinical Hyperthyroidism' 'Clinical Hypothyroidism' 'Subclinical Hyperthyroidism' 'Subclinical Hypothyroidism'] ---------------------------------------- Categorical | Object Column: Physical_Examination ['Single nodular goiter-left' 'Multinodular goiter' 'Single nodular goiter-right' 'Normal' 'Diffuse goiter'] ---------------------------------------- Categorical | Object Column: Adenopathy ['No' 'Right' 'Extensive' 'Left' 'Bilateral' 'Posterior'] ---------------------------------------- Categorical | Object Column: Pathology ['Micropapillary' 'Papillary' 'Follicular' 'Hurthel cell'] ---------------------------------------- Categorical | Object Column: Focality ['Uni-Focal' 'Multi-Focal'] ---------------------------------------- Categorical | Object Column: Risk ['Low' 'Intermediate' 'High'] ---------------------------------------- Categorical | Object Column: T ['T1a' 'T1b' 'T2' 'T3a' 'T3b' 'T4a' 'T4b'] ---------------------------------------- Categorical | Object Column: N ['N0' 'N1b' 'N1a'] ---------------------------------------- Categorical | Object Column: M ['M0' 'M1'] ---------------------------------------- Categorical | Object Column: Stage ['I' 'II' 'IVB' 'III' 'IVA'] ---------------------------------------- Categorical | Object Column: Response ['Indeterminate' 'Excellent' 'Structural Incomplete' 'Biochemical Incomplete'] ---------------------------------------- Categorical | Object Column: Recurred ['No' 'Yes'] ----------------------------------------
In [10]:
##################################
# Correcting a category level
##################################
thyroid_cancer["Pathology"] = thyroid_cancer["Pathology"].replace("Hurthel cell", "Hurthle Cell")
In [11]:
##################################
# Setting the levels of the categorical variables
##################################
thyroid_cancer['Recurred'] = thyroid_cancer['Recurred'].astype('category')
thyroid_cancer['Recurred'] = thyroid_cancer['Recurred'].cat.set_categories(['No', 'Yes'], ordered=True)
thyroid_cancer['Gender'] = thyroid_cancer['Gender'].astype('category')
thyroid_cancer['Gender'] = thyroid_cancer['Gender'].cat.set_categories(['M', 'F'], ordered=True)
thyroid_cancer['Smoking'] = thyroid_cancer['Smoking'].astype('category')
thyroid_cancer['Smoking'] = thyroid_cancer['Smoking'].cat.set_categories(['No', 'Yes'], ordered=True)
thyroid_cancer['Hx_Smoking'] = thyroid_cancer['Hx_Smoking'].astype('category')
thyroid_cancer['Hx_Smoking'] = thyroid_cancer['Hx_Smoking'].cat.set_categories(['No', 'Yes'], ordered=True)
thyroid_cancer['Hx_Radiotherapy'] = thyroid_cancer['Hx_Radiotherapy'].astype('category')
thyroid_cancer['Hx_Radiotherapy'] = thyroid_cancer['Hx_Radiotherapy'].cat.set_categories(['No', 'Yes'], ordered=True)
thyroid_cancer['Thyroid_Function'] = thyroid_cancer['Thyroid_Function'].astype('category')
thyroid_cancer['Thyroid_Function'] = thyroid_cancer['Thyroid_Function'].cat.set_categories(['Euthyroid', 'Subclinical Hypothyroidism', 'Subclinical Hyperthyroidism', 'Clinical Hypothyroidism', 'Clinical Hyperthyroidism'], ordered=True)
thyroid_cancer['Physical_Examination'] = thyroid_cancer['Physical_Examination'].astype('category')
thyroid_cancer['Physical_Examination'] = thyroid_cancer['Physical_Examination'].cat.set_categories(['Normal', 'Single nodular goiter-left', 'Single nodular goiter-right', 'Multinodular goiter', 'Diffuse goiter'], ordered=True)
thyroid_cancer['Adenopathy'] = thyroid_cancer['Adenopathy'].astype('category')
thyroid_cancer['Adenopathy'] = thyroid_cancer['Adenopathy'].cat.set_categories(['No', 'Left', 'Right', 'Bilateral', 'Posterior', 'Extensive'], ordered=True)
thyroid_cancer['Pathology'] = thyroid_cancer['Pathology'].astype('category')
thyroid_cancer['Pathology'] = thyroid_cancer['Pathology'].cat.set_categories(['Hurthle Cell', 'Follicular', 'Micropapillary', 'Papillary'], ordered=True)
thyroid_cancer['Focality'] = thyroid_cancer['Focality'].astype('category')
thyroid_cancer['Focality'] = thyroid_cancer['Focality'].cat.set_categories(['Uni-Focal', 'Multi-Focal'], ordered=True)
thyroid_cancer['Risk'] = thyroid_cancer['Risk'].astype('category')
thyroid_cancer['Risk'] = thyroid_cancer['Risk'].cat.set_categories(['Low', 'Intermediate', 'High'], ordered=True)
thyroid_cancer['T'] = thyroid_cancer['T'].astype('category')
thyroid_cancer['T'] = thyroid_cancer['T'].cat.set_categories(['T1a', 'T1b', 'T2', 'T3a', 'T3b', 'T4a', 'T4b'], ordered=True)
thyroid_cancer['N'] = thyroid_cancer['N'].astype('category')
thyroid_cancer['N'] = thyroid_cancer['N'].cat.set_categories(['N0', 'N1a', 'N1b'], ordered=True)
thyroid_cancer['M'] = thyroid_cancer['M'].astype('category')
thyroid_cancer['M'] = thyroid_cancer['M'].cat.set_categories(['M0', 'M1'], ordered=True)
thyroid_cancer['Stage'] = thyroid_cancer['Stage'].astype('category')
thyroid_cancer['Stage'] = thyroid_cancer['Stage'].cat.set_categories(['I', 'II', 'III', 'IVA', 'IVB'], ordered=True)
thyroid_cancer['Response'] = thyroid_cancer['Response'].astype('category')
thyroid_cancer['Response'] = thyroid_cancer['Response'].cat.set_categories(['Excellent', 'Structural Incomplete', 'Biochemical Incomplete', 'Indeterminate'], ordered=True)
In [12]:
##################################
# Performing a general exploration of the numeric variables
##################################
print('Numeric Variable Summary:')
display(thyroid_cancer.describe(include='number').transpose())
Numeric Variable Summary:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Age | 383.0 | 40.866841 | 15.134494 | 15.0 | 29.0 | 37.0 | 51.0 | 82.0 |
In [13]:
##################################
# Performing a general exploration of the categorical variables
##################################
print('Categorical Variable Summary:')
display(thyroid_cancer.describe(include='category').transpose())
Categorical Variable Summary:
| count | unique | top | freq | |
|---|---|---|---|---|
| Gender | 383 | 2 | F | 312 |
| Smoking | 383 | 2 | No | 334 |
| Hx_Smoking | 383 | 2 | No | 355 |
| Hx_Radiotherapy | 383 | 2 | No | 376 |
| Thyroid_Function | 383 | 5 | Euthyroid | 332 |
| Physical_Examination | 383 | 5 | Single nodular goiter-right | 140 |
| Adenopathy | 383 | 6 | No | 277 |
| Pathology | 383 | 4 | Papillary | 287 |
| Focality | 383 | 2 | Uni-Focal | 247 |
| Risk | 383 | 3 | Low | 249 |
| T | 383 | 7 | T2 | 151 |
| N | 383 | 3 | N0 | 268 |
| M | 383 | 2 | M0 | 365 |
| Stage | 383 | 5 | I | 333 |
| Response | 383 | 4 | Excellent | 208 |
| Recurred | 383 | 2 | No | 275 |
In [14]:
##################################
# Performing a general exploration of the categorical variable levels
# based on the ordered categories
##################################
ordered_cat_cols = thyroid_cancer.select_dtypes(include=["category"]).columns
for col in ordered_cat_cols:
print(f"Column: {col}")
print("Absolute Frequencies:")
print(thyroid_cancer[col].value_counts().reindex(thyroid_cancer[col].cat.categories))
print("\nNormalized Frequencies:")
print(thyroid_cancer[col].value_counts(normalize=True).reindex(thyroid_cancer[col].cat.categories))
print("-" * 50)
Column: Gender Absolute Frequencies: M 71 F 312 Name: count, dtype: int64 Normalized Frequencies: M 0.185379 F 0.814621 Name: proportion, dtype: float64 -------------------------------------------------- Column: Smoking Absolute Frequencies: No 334 Yes 49 Name: count, dtype: int64 Normalized Frequencies: No 0.872063 Yes 0.127937 Name: proportion, dtype: float64 -------------------------------------------------- Column: Hx_Smoking Absolute Frequencies: No 355 Yes 28 Name: count, dtype: int64 Normalized Frequencies: No 0.926893 Yes 0.073107 Name: proportion, dtype: float64 -------------------------------------------------- Column: Hx_Radiotherapy Absolute Frequencies: No 376 Yes 7 Name: count, dtype: int64 Normalized Frequencies: No 0.981723 Yes 0.018277 Name: proportion, dtype: float64 -------------------------------------------------- Column: Thyroid_Function Absolute Frequencies: Euthyroid 332 Subclinical Hypothyroidism 14 Subclinical Hyperthyroidism 5 Clinical Hypothyroidism 12 Clinical Hyperthyroidism 20 Name: count, dtype: int64 Normalized Frequencies: Euthyroid 0.866841 Subclinical Hypothyroidism 0.036554 Subclinical Hyperthyroidism 0.013055 Clinical Hypothyroidism 0.031332 Clinical Hyperthyroidism 0.052219 Name: proportion, dtype: float64 -------------------------------------------------- Column: Physical_Examination Absolute Frequencies: Normal 7 Single nodular goiter-left 89 Single nodular goiter-right 140 Multinodular goiter 140 Diffuse goiter 7 Name: count, dtype: int64 Normalized Frequencies: Normal 0.018277 Single nodular goiter-left 0.232376 Single nodular goiter-right 0.365535 Multinodular goiter 0.365535 Diffuse goiter 0.018277 Name: proportion, dtype: float64 -------------------------------------------------- Column: Adenopathy Absolute Frequencies: No 277 Left 17 Right 48 Bilateral 32 Posterior 2 Extensive 7 Name: count, dtype: int64 Normalized Frequencies: No 0.723238 Left 0.044386 Right 0.125326 Bilateral 0.083551 Posterior 0.005222 Extensive 0.018277 Name: proportion, dtype: float64 -------------------------------------------------- Column: Pathology Absolute Frequencies: Hurthle Cell 20 Follicular 28 Micropapillary 48 Papillary 287 Name: count, dtype: int64 Normalized Frequencies: Hurthle Cell 0.052219 Follicular 0.073107 Micropapillary 0.125326 Papillary 0.749347 Name: proportion, dtype: float64 -------------------------------------------------- Column: Focality Absolute Frequencies: Uni-Focal 247 Multi-Focal 136 Name: count, dtype: int64 Normalized Frequencies: Uni-Focal 0.644909 Multi-Focal 0.355091 Name: proportion, dtype: float64 -------------------------------------------------- Column: Risk Absolute Frequencies: Low 249 Intermediate 102 High 32 Name: count, dtype: int64 Normalized Frequencies: Low 0.650131 Intermediate 0.266319 High 0.083551 Name: proportion, dtype: float64 -------------------------------------------------- Column: T Absolute Frequencies: T1a 49 T1b 43 T2 151 T3a 96 T3b 16 T4a 20 T4b 8 Name: count, dtype: int64 Normalized Frequencies: T1a 0.127937 T1b 0.112272 T2 0.394256 T3a 0.250653 T3b 0.041775 T4a 0.052219 T4b 0.020888 Name: proportion, dtype: float64 -------------------------------------------------- Column: N Absolute Frequencies: N0 268 N1a 22 N1b 93 Name: count, dtype: int64 Normalized Frequencies: N0 0.699739 N1a 0.057441 N1b 0.242820 Name: proportion, dtype: float64 -------------------------------------------------- Column: M Absolute Frequencies: M0 365 M1 18 Name: count, dtype: int64 Normalized Frequencies: M0 0.953003 M1 0.046997 Name: proportion, dtype: float64 -------------------------------------------------- Column: Stage Absolute Frequencies: I 333 II 32 III 4 IVA 3 IVB 11 Name: count, dtype: int64 Normalized Frequencies: I 0.869452 II 0.083551 III 0.010444 IVA 0.007833 IVB 0.028721 Name: proportion, dtype: float64 -------------------------------------------------- Column: Response Absolute Frequencies: Excellent 208 Structural Incomplete 91 Biochemical Incomplete 23 Indeterminate 61 Name: count, dtype: int64 Normalized Frequencies: Excellent 0.543081 Structural Incomplete 0.237598 Biochemical Incomplete 0.060052 Indeterminate 0.159269 Name: proportion, dtype: float64 -------------------------------------------------- Column: Recurred Absolute Frequencies: No 275 Yes 108 Name: count, dtype: int64 Normalized Frequencies: No 0.718016 Yes 0.281984 Name: proportion, dtype: float64 --------------------------------------------------
1.3. Data Quality Assessment ¶
Data quality findings based on assessment are as follows:
- A total of 19 duplicated rows were identified.
- In total, 34 observations were affected, consisting of 16 unique occurrences and 19 subsequent duplicates.
- These 19 duplicates spanned 16 distinct variations, meaning some variations had multiple duplicates.
- To clean the dataset, all 19 duplicate rows were removed, retaining only the first occurrence of each of the 16 unique variations.
- No missing data noted for any variable with Null.Count>0 and Fill.Rate<1.0.
- Low variance observed for 8 variables with First.Second.Mode.Ratio>5.
- Hx_Radiotherapy: First.Second.Mode.Ratio = 51.000 (comprised 2 category levels)
- M: First.Second.Mode.Ratio = 19.222 (comprised 2 category levels)
- Thyroid_Function: First.Second.Mode.Ratio = 15.650 (comprised 5 category levels)
- Hx_Smoking: First.Second.Mode.Ratio = 12.000 (comprised 2 category levels)
- Stage: First.Second.Mode.Ratio = 9.812 (comprised 5 category levels)
- Smoking: First.Second.Mode.Ratio = 6.428 (comprised 2 category levels)
- Pathology: First.Second.Mode.Ratio = 6.022 (comprised 4 category levels)
- Adenopathy: First.Second.Mode.Ratio = 5.375 (comprised 5 category levels)
- No low variance observed for any variable with Unique.Count.Ratio>10.
- No high skewness observed for any variable with Skewness>3 or Skewness<(-3).
In [15]:
##################################
# Counting the number of duplicated rows
##################################
thyroid_cancer.duplicated().sum()
Out[15]:
np.int64(19)
In [16]:
##################################
# Exploring the duplicated rows
##################################
duplicated_rows = thyroid_cancer[thyroid_cancer.duplicated(keep=False)]
display(duplicated_rows)
| Age | Gender | Smoking | Hx_Smoking | Hx_Radiotherapy | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | M | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 9 | 40.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 22 | 36.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 32 | 36.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 38 | 40.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 40 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No |
| 61 | 35.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 66 | 35.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 67 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-left | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 69 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-left | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 73 | 29.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 77 | 29.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No |
| 106 | 26.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 110 | 31.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 113 | 32.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 115 | 37.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 119 | 28.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 120 | 37.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 121 | 26.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 123 | 28.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 132 | 32.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 136 | 21.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 137 | 32.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 138 | 26.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 142 | 42.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 161 | 22.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 166 | 31.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 168 | 21.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 170 | 38.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 175 | 34.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 178 | 38.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 183 | 26.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 187 | 34.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 189 | 42.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
| 196 | 22.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No |
In [17]:
##################################
# Checking if duplicated rows have identical values across all columns
##################################
num_unique_dup_rows = duplicated_rows.drop_duplicates().shape[0]
num_total_dup_rows = duplicated_rows.shape[0]
if num_unique_dup_rows == 1:
print("All duplicated rows have the same values across all columns.")
else:
print(f"There are {num_unique_dup_rows} unique versions among the {num_total_dup_rows} duplicated rows.")
There are 16 unique versions among the 35 duplicated rows.
In [18]:
##################################
# Counting the unique variations among duplicated rows
##################################
unique_dup_variations = duplicated_rows.drop_duplicates()
variation_counts = duplicated_rows.value_counts().reset_index(name="Count")
print("Unique duplicated row variations and their counts:")
display(variation_counts)
Unique duplicated row variations and their counts:
| Age | Gender | Smoking | Hx_Smoking | Hx_Radiotherapy | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | M | Stage | Response | Recurred | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 26.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 4 |
| 1 | 32.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 3 |
| 2 | 22.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 3 | 21.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 4 | 28.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 5 | 29.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No | 2 |
| 6 | 31.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 7 | 34.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 8 | 35.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No | 2 |
| 9 | 36.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No | 2 |
| 10 | 37.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 11 | 38.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 12 | 40.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No | 2 |
| 13 | 42.0 | F | No | No | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | M0 | I | Excellent | No | 2 |
| 14 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-left | No | Papillary | Uni-Focal | Low | T1b | N0 | M0 | I | Excellent | No | 2 |
| 15 | 51.0 | F | No | No | No | Euthyroid | Single nodular goiter-right | No | Micropapillary | Uni-Focal | Low | T1a | N0 | M0 | I | Excellent | No | 2 |
In [19]:
##################################
# Removing the duplicated rows and
# retaining only the first occurrence
##################################
thyroid_cancer_row_filtered = thyroid_cancer.drop_duplicates(keep="first")
print('Dataset Dimensions: ')
display(thyroid_cancer_row_filtered.shape)
Dataset Dimensions:
(364, 17)
In [20]:
##################################
# Gathering the data types for each column
##################################
data_type_list = list(thyroid_cancer_row_filtered.dtypes)
In [21]:
##################################
# Gathering the variable names for each column
##################################
variable_name_list = list(thyroid_cancer_row_filtered.columns)
In [22]:
##################################
# Gathering the number of observations for each column
##################################
row_count_list = list([len(thyroid_cancer_row_filtered)] * len(thyroid_cancer_row_filtered.columns))
In [23]:
##################################
# Gathering the number of missing data for each column
##################################
null_count_list = list(thyroid_cancer_row_filtered.isna().sum(axis=0))
In [24]:
##################################
# Gathering the number of non-missing data for each column
##################################
non_null_count_list = list(thyroid_cancer_row_filtered.count())
In [25]:
##################################
# Gathering the missing data percentage for each column
##################################
fill_rate_list = map(truediv, non_null_count_list, row_count_list)
In [26]:
##################################
# Formulating the summary
# for all columns
##################################
all_column_quality_summary = pd.DataFrame(zip(variable_name_list,
data_type_list,
row_count_list,
non_null_count_list,
null_count_list,
fill_rate_list),
columns=['Column.Name',
'Column.Type',
'Row.Count',
'Non.Null.Count',
'Null.Count',
'Fill.Rate'])
display(all_column_quality_summary)
| Column.Name | Column.Type | Row.Count | Non.Null.Count | Null.Count | Fill.Rate | |
|---|---|---|---|---|---|---|
| 0 | Age | float64 | 364 | 364 | 0 | 1.0 |
| 1 | Gender | category | 364 | 364 | 0 | 1.0 |
| 2 | Smoking | category | 364 | 364 | 0 | 1.0 |
| 3 | Hx_Smoking | category | 364 | 364 | 0 | 1.0 |
| 4 | Hx_Radiotherapy | category | 364 | 364 | 0 | 1.0 |
| 5 | Thyroid_Function | category | 364 | 364 | 0 | 1.0 |
| 6 | Physical_Examination | category | 364 | 364 | 0 | 1.0 |
| 7 | Adenopathy | category | 364 | 364 | 0 | 1.0 |
| 8 | Pathology | category | 364 | 364 | 0 | 1.0 |
| 9 | Focality | category | 364 | 364 | 0 | 1.0 |
| 10 | Risk | category | 364 | 364 | 0 | 1.0 |
| 11 | T | category | 364 | 364 | 0 | 1.0 |
| 12 | N | category | 364 | 364 | 0 | 1.0 |
| 13 | M | category | 364 | 364 | 0 | 1.0 |
| 14 | Stage | category | 364 | 364 | 0 | 1.0 |
| 15 | Response | category | 364 | 364 | 0 | 1.0 |
| 16 | Recurred | category | 364 | 364 | 0 | 1.0 |
In [27]:
##################################
# Counting the number of columns
# with Fill.Rate < 1.00
##################################
len(all_column_quality_summary[(all_column_quality_summary['Fill.Rate']<1)])
Out[27]:
0
In [28]:
##################################
# Identifying the rows
# with Fill.Rate < 0.90
##################################
column_low_fill_rate = all_column_quality_summary[(all_column_quality_summary['Fill.Rate']<0.90)]
In [29]:
##################################
# Gathering the indices for each observation
##################################
row_index_list = thyroid_cancer_row_filtered.index
In [30]:
##################################
# Gathering the number of columns for each observation
##################################
column_count_list = list([len(thyroid_cancer_row_filtered.columns)] * len(thyroid_cancer_row_filtered))
In [31]:
##################################
# Gathering the number of missing data for each row
##################################
null_row_list = list(thyroid_cancer_row_filtered.isna().sum(axis=1))
In [32]:
##################################
# Gathering the missing data percentage for each column
##################################
missing_rate_list = map(truediv, null_row_list, column_count_list)
In [33]:
##################################
# Identifying the rows
# with missing data
##################################
all_row_quality_summary = pd.DataFrame(zip(row_index_list,
column_count_list,
null_row_list,
missing_rate_list),
columns=['Row.Name',
'Column.Count',
'Null.Count',
'Missing.Rate'])
display(all_row_quality_summary)
| Row.Name | Column.Count | Null.Count | Missing.Rate | |
|---|---|---|---|---|
| 0 | 0 | 17 | 0 | 0.0 |
| 1 | 1 | 17 | 0 | 0.0 |
| 2 | 2 | 17 | 0 | 0.0 |
| 3 | 3 | 17 | 0 | 0.0 |
| 4 | 4 | 17 | 0 | 0.0 |
| ... | ... | ... | ... | ... |
| 359 | 378 | 17 | 0 | 0.0 |
| 360 | 379 | 17 | 0 | 0.0 |
| 361 | 380 | 17 | 0 | 0.0 |
| 362 | 381 | 17 | 0 | 0.0 |
| 363 | 382 | 17 | 0 | 0.0 |
364 rows × 4 columns
In [34]:
##################################
# Counting the number of rows
# with Missing.Rate > 0.00
##################################
len(all_row_quality_summary[(all_row_quality_summary['Missing.Rate']>0.00)])
Out[34]:
0
In [35]:
##################################
# Formulating the dataset
# with numeric columns only
##################################
thyroid_cancer_numeric = thyroid_cancer_row_filtered.select_dtypes(include='number')
In [36]:
##################################
# Gathering the variable names for each numeric column
##################################
numeric_variable_name_list = thyroid_cancer_numeric.columns
In [37]:
##################################
# Gathering the minimum value for each numeric column
##################################
numeric_minimum_list = thyroid_cancer_numeric.min()
In [38]:
##################################
# Gathering the mean value for each numeric column
##################################
numeric_mean_list = thyroid_cancer_numeric.mean()
In [39]:
##################################
# Gathering the median value for each numeric column
##################################
numeric_median_list = thyroid_cancer_numeric.median()
In [40]:
##################################
# Gathering the maximum value for each numeric column
##################################
numeric_maximum_list = thyroid_cancer_numeric.max()
In [41]:
##################################
# Gathering the first mode values for each numeric column
##################################
numeric_first_mode_list = [thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[0] for x in thyroid_cancer_numeric]
In [42]:
##################################
# Gathering the second mode values for each numeric column
##################################
numeric_second_mode_list = [thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[1] for x in thyroid_cancer_numeric]
In [43]:
##################################
# Gathering the count of first mode values for each numeric column
##################################
numeric_first_mode_count_list = [thyroid_cancer_numeric[x].isin([thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[0]]).sum() for x in thyroid_cancer_numeric]
In [44]:
##################################
# Gathering the count of second mode values for each numeric column
##################################
numeric_second_mode_count_list = [thyroid_cancer_numeric[x].isin([thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[1]]).sum() for x in thyroid_cancer_numeric]
In [45]:
##################################
# Gathering the first mode to second mode ratio for each numeric column
##################################
numeric_first_second_mode_ratio_list = map(truediv, numeric_first_mode_count_list, numeric_second_mode_count_list)
In [46]:
##################################
# Gathering the count of unique values for each numeric column
##################################
numeric_unique_count_list = thyroid_cancer_numeric.nunique(dropna=True)
In [47]:
##################################
# Gathering the number of observations for each numeric column
##################################
numeric_row_count_list = list([len(thyroid_cancer_numeric)] * len(thyroid_cancer_numeric.columns))
In [48]:
##################################
# Gathering the unique to count ratio for each numeric column
##################################
numeric_unique_count_ratio_list = map(truediv, numeric_unique_count_list, numeric_row_count_list)
In [49]:
##################################
# Gathering the skewness value for each numeric column
##################################
numeric_skewness_list = thyroid_cancer_numeric.skew()
In [50]:
##################################
# Gathering the kurtosis value for each numeric column
##################################
numeric_kurtosis_list = thyroid_cancer_numeric.kurtosis()
In [51]:
##################################
# Generating a column quality summary for the numeric column
##################################
numeric_column_quality_summary = pd.DataFrame(zip(numeric_variable_name_list,
numeric_minimum_list,
numeric_mean_list,
numeric_median_list,
numeric_maximum_list,
numeric_first_mode_list,
numeric_second_mode_list,
numeric_first_mode_count_list,
numeric_second_mode_count_list,
numeric_first_second_mode_ratio_list,
numeric_unique_count_list,
numeric_row_count_list,
numeric_unique_count_ratio_list,
numeric_skewness_list,
numeric_kurtosis_list),
columns=['Numeric.Column.Name',
'Minimum',
'Mean',
'Median',
'Maximum',
'First.Mode',
'Second.Mode',
'First.Mode.Count',
'Second.Mode.Count',
'First.Second.Mode.Ratio',
'Unique.Count',
'Row.Count',
'Unique.Count.Ratio',
'Skewness',
'Kurtosis'])
display(numeric_column_quality_summary)
| Numeric.Column.Name | Minimum | Mean | Median | Maximum | First.Mode | Second.Mode | First.Mode.Count | Second.Mode.Count | First.Second.Mode.Ratio | Unique.Count | Row.Count | Unique.Count.Ratio | Skewness | Kurtosis | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Age | 15.0 | 41.25 | 38.0 | 82.0 | 31.0 | 27.0 | 21 | 13 | 1.615385 | 65 | 364 | 0.178571 | 0.678269 | -0.359255 |
In [52]:
##################################
# Counting the number of numeric columns
# with First.Second.Mode.Ratio > 5.00
##################################
len(numeric_column_quality_summary[(numeric_column_quality_summary['First.Second.Mode.Ratio']>5)])
Out[52]:
0
In [53]:
##################################
# Counting the number of numeric columns
# with Unique.Count.Ratio > 10.00
##################################
len(numeric_column_quality_summary[(numeric_column_quality_summary['Unique.Count.Ratio']>10)])
Out[53]:
0
In [54]:
##################################
# Counting the number of numeric columns
# with Skewness > 3.00 or Skewness < -3.00
##################################
len(numeric_column_quality_summary[(numeric_column_quality_summary['Skewness']>3) | (numeric_column_quality_summary['Skewness']<(-3))])
Out[54]:
0
In [55]:
##################################
# Formulating the dataset
# with categorical columns only
##################################
thyroid_cancer_categorical = thyroid_cancer_row_filtered.select_dtypes(include='category')
In [56]:
##################################
# Gathering the variable names for the categorical column
##################################
categorical_variable_name_list = thyroid_cancer_categorical.columns
In [57]:
##################################
# Gathering the first mode values for each categorical column
##################################
categorical_first_mode_list = [thyroid_cancer_row_filtered[x].value_counts().index.tolist()[0] for x in thyroid_cancer_categorical]
In [58]:
##################################
# Gathering the second mode values for each categorical column
##################################
categorical_second_mode_list = [thyroid_cancer_row_filtered[x].value_counts().index.tolist()[1] for x in thyroid_cancer_categorical]
In [59]:
##################################
# Gathering the count of first mode values for each categorical column
##################################
categorical_first_mode_count_list = [thyroid_cancer_categorical[x].isin([thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[0]]).sum() for x in thyroid_cancer_categorical]
In [60]:
##################################
# Gathering the count of second mode values for each categorical column
##################################
categorical_second_mode_count_list = [thyroid_cancer_categorical[x].isin([thyroid_cancer_row_filtered[x].value_counts(dropna=True).index.tolist()[1]]).sum() for x in thyroid_cancer_categorical]
In [61]:
##################################
# Gathering the first mode to second mode ratio for each categorical column
##################################
categorical_first_second_mode_ratio_list = map(truediv, categorical_first_mode_count_list, categorical_second_mode_count_list)
In [62]:
##################################
# Gathering the count of unique values for each categorical column
##################################
categorical_unique_count_list = thyroid_cancer_categorical.nunique(dropna=True)
In [63]:
##################################
# Gathering the number of observations for each categorical column
##################################
categorical_row_count_list = list([len(thyroid_cancer_categorical)] * len(thyroid_cancer_categorical.columns))
In [64]:
##################################
# Gathering the unique to count ratio for each categorical column
##################################
categorical_unique_count_ratio_list = map(truediv, categorical_unique_count_list, categorical_row_count_list)
In [65]:
##################################
# Generating a column quality summary for the categorical columns
##################################
categorical_column_quality_summary = pd.DataFrame(zip(categorical_variable_name_list,
categorical_first_mode_list,
categorical_second_mode_list,
categorical_first_mode_count_list,
categorical_second_mode_count_list,
categorical_first_second_mode_ratio_list,
categorical_unique_count_list,
categorical_row_count_list,
categorical_unique_count_ratio_list),
columns=['Categorical.Column.Name',
'First.Mode',
'Second.Mode',
'First.Mode.Count',
'Second.Mode.Count',
'First.Second.Mode.Ratio',
'Unique.Count',
'Row.Count',
'Unique.Count.Ratio'])
display(categorical_column_quality_summary)
| Categorical.Column.Name | First.Mode | Second.Mode | First.Mode.Count | Second.Mode.Count | First.Second.Mode.Ratio | Unique.Count | Row.Count | Unique.Count.Ratio | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Gender | F | M | 293 | 71 | 4.126761 | 2 | 364 | 0.005495 |
| 1 | Smoking | No | Yes | 315 | 49 | 6.428571 | 2 | 364 | 0.005495 |
| 2 | Hx_Smoking | No | Yes | 336 | 28 | 12.000000 | 2 | 364 | 0.005495 |
| 3 | Hx_Radiotherapy | No | Yes | 357 | 7 | 51.000000 | 2 | 364 | 0.005495 |
| 4 | Thyroid_Function | Euthyroid | Clinical Hyperthyroidism | 313 | 20 | 15.650000 | 5 | 364 | 0.013736 |
| 5 | Physical_Examination | Multinodular goiter | Single nodular goiter-right | 135 | 127 | 1.062992 | 5 | 364 | 0.013736 |
| 6 | Adenopathy | No | Right | 258 | 48 | 5.375000 | 6 | 364 | 0.016484 |
| 7 | Pathology | Papillary | Micropapillary | 271 | 45 | 6.022222 | 4 | 364 | 0.010989 |
| 8 | Focality | Uni-Focal | Multi-Focal | 228 | 136 | 1.676471 | 2 | 364 | 0.005495 |
| 9 | Risk | Low | Intermediate | 230 | 102 | 2.254902 | 3 | 364 | 0.008242 |
| 10 | T | T2 | T3a | 138 | 96 | 1.437500 | 7 | 364 | 0.019231 |
| 11 | N | N0 | N1b | 249 | 93 | 2.677419 | 3 | 364 | 0.008242 |
| 12 | M | M0 | M1 | 346 | 18 | 19.222222 | 2 | 364 | 0.005495 |
| 13 | Stage | I | II | 314 | 32 | 9.812500 | 5 | 364 | 0.013736 |
| 14 | Response | Excellent | Structural Incomplete | 189 | 91 | 2.076923 | 4 | 364 | 0.010989 |
| 15 | Recurred | No | Yes | 256 | 108 | 2.370370 | 2 | 364 | 0.005495 |
In [66]:
##################################
# Counting the number of categorical columns
# with First.Second.Mode.Ratio > 5.00
##################################
len(categorical_column_quality_summary[(categorical_column_quality_summary['First.Second.Mode.Ratio']>5)])
Out[66]:
8
In [67]:
##################################
# Identifying the categorical columns
# with First.Second.Mode.Ratio > 5.00
##################################
display(categorical_column_quality_summary[(categorical_column_quality_summary['First.Second.Mode.Ratio']>5)].sort_values(by=['First.Second.Mode.Ratio'], ascending=False))
| Categorical.Column.Name | First.Mode | Second.Mode | First.Mode.Count | Second.Mode.Count | First.Second.Mode.Ratio | Unique.Count | Row.Count | Unique.Count.Ratio | |
|---|---|---|---|---|---|---|---|---|---|
| 3 | Hx_Radiotherapy | No | Yes | 357 | 7 | 51.000000 | 2 | 364 | 0.005495 |
| 12 | M | M0 | M1 | 346 | 18 | 19.222222 | 2 | 364 | 0.005495 |
| 4 | Thyroid_Function | Euthyroid | Clinical Hyperthyroidism | 313 | 20 | 15.650000 | 5 | 364 | 0.013736 |
| 2 | Hx_Smoking | No | Yes | 336 | 28 | 12.000000 | 2 | 364 | 0.005495 |
| 13 | Stage | I | II | 314 | 32 | 9.812500 | 5 | 364 | 0.013736 |
| 1 | Smoking | No | Yes | 315 | 49 | 6.428571 | 2 | 364 | 0.005495 |
| 7 | Pathology | Papillary | Micropapillary | 271 | 45 | 6.022222 | 4 | 364 | 0.010989 |
| 6 | Adenopathy | No | Right | 258 | 48 | 5.375000 | 6 | 364 | 0.016484 |
In [68]:
##################################
# Counting the number of categorical columns
# with Unique.Count.Ratio > 10.00
##################################
len(categorical_column_quality_summary[(categorical_column_quality_summary['Unique.Count.Ratio']>10)])
Out[68]:
0
1.4. Data Preprocessing ¶
1.4.1 Data Splitting¶
- The baseline dataset (with duplicate rows removed from the original dataset) is comprised of:
- 364 rows (observations)
- 256 Recurred=No: 70.33%
- 108 Recurred=Yes: 29.67%
- 17 columns (variables)
- 1/17 target (categorical)
- Recurred
- 1/17 predictor (numeric)
- Age
- 15/17 predictor (categorical)
- Gender
- Smoking
- Hx_Smoking
- Hx_Radiotherapy
- Thyroid_Function
- Physical_Examination
- Adenopathy
- Pathology
- Focality
- Risk
- T
- N
- M
- Stage
- Response
- 1/17 target (categorical)
- 364 rows (observations)
- The baseline dataset was divided into three subsets using a fixed random seed:
- test data: 25% of the original data with class stratification applied
- train data (initial): 75% of the original data with class stratification applied
- train data (final): 75% of the train (initial) data with class stratification applied
- validation data: 25% of the train (initial) data with class stratification applied
- Models were developed from the train data (final). Using the same dataset, a subset of models with optimal hyperparameters were selected, based on cross-validation.
- Among candidate models with optimal hyperparameters, the final model were selected based on performance on the validation data.
- Performance of the selected final model (and other candidate models for post-model selection comparison) were evaluated using the test data.
- The train data (final) subset is comprised of:
- 204 rows (observations)
- 143 Recurred=No: 70.10%
- 61 Recurred=Yes: 29.90%
- 17 columns (variables)
- 204 rows (observations)
- The validation data subset is comprised of:
- 69 rows (observations)
- 49 Recurred=No: 71.01%
- 20 Recurred=Yes: 28.98%
- 17 columns (variables)
- 69 rows (observations)
- The test data subset is comprised of:
- 91 rows (observations)
- 64 Recurred=No: 70.33%
- 27 Recurred=Yes: 29.67%
- 17 columns (variables)
- 91 rows (observations)
In [69]:
##################################
# Creating a dataset copy
# of the row filtered data
##################################
thyroid_cancer_baseline = thyroid_cancer_row_filtered.copy()
In [70]:
##################################
# Performing a general exploration
# of the baseline dataset
##################################
print('Final Dataset Dimensions: ')
display(thyroid_cancer_baseline.shape)
Final Dataset Dimensions:
(364, 17)
In [71]:
##################################
# Obtaining the distribution of
# of the target variable
##################################
print('Target Variable Breakdown: ')
thyroid_cancer_breakdown = thyroid_cancer_baseline.groupby('Recurred', observed=True).size().reset_index(name='Count')
thyroid_cancer_breakdown['Percentage'] = (thyroid_cancer_breakdown['Count'] / len(thyroid_cancer_baseline)) * 100
display(thyroid_cancer_breakdown)
Target Variable Breakdown:
| Recurred | Count | Percentage | |
|---|---|---|---|
| 0 | No | 256 | 70.32967 |
| 1 | Yes | 108 | 29.67033 |
In [72]:
##################################
# Formulating the train and test data
# from the final dataset
# by applying stratification and
# using a 75-25 ratio
##################################
thyroid_cancer_train_initial, thyroid_cancer_test = train_test_split(thyroid_cancer_baseline,
test_size=0.25,
stratify=thyroid_cancer_baseline['Recurred'],
random_state=987654321)
In [73]:
##################################
# Performing a general exploration
# of the initial training dataset
##################################
X_train_initial = thyroid_cancer_train_initial.drop('Recurred', axis = 1)
y_train_initial = thyroid_cancer_train_initial['Recurred']
print('Initial Train Dataset Dimensions: ')
display(X_train_initial.shape)
display(y_train_initial.shape)
print('Initial Train Target Variable Breakdown: ')
display(y_train_initial.value_counts())
print('Initial Train Target Variable Proportion: ')
display(y_train_initial.value_counts(normalize = True))
Initial Train Dataset Dimensions:
(273, 16)
(273,)
Initial Train Target Variable Breakdown:
Recurred No 192 Yes 81 Name: count, dtype: int64
Initial Train Target Variable Proportion:
Recurred No 0.703297 Yes 0.296703 Name: proportion, dtype: float64
In [74]:
##################################
# Performing a general exploration
# of the test dataset
##################################
X_test = thyroid_cancer_test.drop('Recurred', axis = 1)
y_test = thyroid_cancer_test['Recurred']
print('Test Dataset Dimensions: ')
display(X_test.shape)
display(y_test.shape)
print('Test Target Variable Breakdown: ')
display(y_test.value_counts())
print('Test Target Variable Proportion: ')
display(y_test.value_counts(normalize = True))
Test Dataset Dimensions:
(91, 16)
(91,)
Test Target Variable Breakdown:
Recurred No 64 Yes 27 Name: count, dtype: int64
Test Target Variable Proportion:
Recurred No 0.703297 Yes 0.296703 Name: proportion, dtype: float64
In [75]:
##################################
# Formulating the train and validation data
# from the train dataset
# by applying stratification and
# using a 75-25 ratio
##################################
thyroid_cancer_train, thyroid_cancer_validation = train_test_split(thyroid_cancer_train_initial,
test_size=0.25,
stratify=thyroid_cancer_train_initial['Recurred'],
random_state=987654321)
In [76]:
##################################
# Performing a general exploration
# of the final training dataset
##################################
X_train = thyroid_cancer_train.drop('Recurred', axis = 1)
y_train = thyroid_cancer_train['Recurred']
print('Final Train Dataset Dimensions: ')
display(X_train.shape)
display(y_train.shape)
print('Final Train Target Variable Breakdown: ')
display(y_train.value_counts())
print('Final Train Target Variable Proportion: ')
display(y_train.value_counts(normalize = True))
Final Train Dataset Dimensions:
(204, 16)
(204,)
Final Train Target Variable Breakdown:
Recurred No 143 Yes 61 Name: count, dtype: int64
Final Train Target Variable Proportion:
Recurred No 0.70098 Yes 0.29902 Name: proportion, dtype: float64
In [77]:
##################################
# Performing a general exploration
# of the validation dataset
##################################
X_validation = thyroid_cancer_validation.drop('Recurred', axis = 1)
y_validation = thyroid_cancer_validation['Recurred']
print('Validation Dataset Dimensions: ')
display(X_validation.shape)
display(y_validation.shape)
print('Validation Target Variable Breakdown: ')
display(y_validation.value_counts())
print('Validation Target Variable Proportion: ')
display(y_validation.value_counts(normalize = True))
Validation Dataset Dimensions:
(69, 16)
(69,)
Validation Target Variable Breakdown:
Recurred No 49 Yes 20 Name: count, dtype: int64
Validation Target Variable Proportion:
Recurred No 0.710145 Yes 0.289855 Name: proportion, dtype: float64
In [78]:
##################################
# Saving the training data
# to the DATASETS_FINAL_TRAIN_PATH
# and DATASETS_FINAL_TRAIN_FEATURES_PATH
# and DATASETS_FINAL_TRAIN_TARGET_PATH
##################################
thyroid_cancer_train.to_csv(os.path.join("..", DATASETS_FINAL_TRAIN_PATH, "thyroid_cancer_train.csv"), index=False)
X_train.to_csv(os.path.join("..", DATASETS_FINAL_TRAIN_FEATURES_PATH, "X_train.csv"), index=False)
y_train.to_csv(os.path.join("..", DATASETS_FINAL_TRAIN_TARGET_PATH, "y_train.csv"), index=False)
In [79]:
##################################
# Saving the validation data
# to the DATASETS_FINAL_VALIDATION_PATH
# and DATASETS_FINAL_VALIDATION_FEATURE_PATH
# and DATASETS_FINAL_VALIDATION_TARGET_PATH
##################################
thyroid_cancer_validation.to_csv(os.path.join("..", DATASETS_FINAL_VALIDATION_PATH, "thyroid_cancer_validation.csv"), index=False)
X_validation.to_csv(os.path.join("..", DATASETS_FINAL_VALIDATION_FEATURES_PATH, "X_validation.csv"), index=False)
y_validation.to_csv(os.path.join("..", DATASETS_FINAL_VALIDATION_TARGET_PATH, "y_validation.csv"), index=False)
In [80]:
##################################
# Saving the test data
# to the DATASETS_FINAL_TEST_PATH
# and DATASETS_FINAL_TEST_FEATURES_PATH
# and DATASETS_FINAL_TEST_TARGET_PATH
##################################
thyroid_cancer_test.to_csv(os.path.join("..", DATASETS_FINAL_TEST_PATH, "thyroid_cancer_test.csv"), index=False)
X_test.to_csv(os.path.join("..", DATASETS_FINAL_TEST_FEATURES_PATH, "X_test.csv"), index=False)
y_test.to_csv(os.path.join("..", DATASETS_FINAL_TEST_TARGET_PATH, "y_test.csv"), index=False)
1.4.2 Data Profiling¶
- No significant distributional anomalies were observed for the numeric predictor Age.
- 9 categorical predictors were observed with categories consisting of too few cases that risk poor generalization and cross-validation issues:
- Thyroid_Function:
- 171 Thyroid_Function=Euthyroid: 83.82%
- 10 Thyroid_Function=Subclinical Hypothyroidism: 4.90%
- 3 Thyroid_Function=Subclinical Hyperthyroidism: 1.47%
- 7 Thyroid_Function=Clinical Hypothyroidism: 3.43%
- 13 Thyroid_Function=Clinical Hyperthyroidism: 6.37%
- Physical_Examination:
- 4 Physical_Examination=Normal: 1.96%
- 50 Physical_Examination=Single nodular goiter-left: 24.50%
- 68 Physical_Examination=Single nodular goiter-right: 33.33%
- 79 Physical_Examination=Multinodular goiter: 38.72%
- 3 Physical_Examination=Diffuse goiter: 1.47%
- Adenopathy:
- 144 Adenopathy=No: 70.59%
- 14 Adenopathy=Left: 6.86%
- 21 Adenopathy=Right: 10.29%
- 19 Adenopathy=Bilateral: 9.31%
- 2 Adenopathy=Posterior: 9.84%
- 4 Adenopathy=Extensive: 1.96%
- Pathology:
- 15 Pathology=Hurthle Cell: 7.35%
- 14 Pathology=Follicular: 6.86%
- 26 Pathology=Micropapillary: 12.74%
- 149 Pathology=Papillary: 73.03%
- Risk:
- 127 Risk=Low: 62.25%
- 60 Risk=Intermediate: 29.41%
- 17 Risk=High: 8.33%
- T:
- 26 T=T1a: 12.74%
- 21 T=T1b: 10.29%
- 73 T=T2: 35.78%
- 58 T=T3a: 28.43%
- 10 T=T3b: 4.90%
- 12 T=T4a: 5.88%
- 4 T=T4b: 1.96%
- N:
- 139 N=N0: 68.13%
- 11 N=N1a: 5.39%
- 54 N=N1b: 26.47%
- Stage:
- 174 Stage=I: 85.29%
- 21 Stage=II: 10.29%
- 2 Stage=III: 0.98%
- 2 Stage=IVA: 0.98%
- 5 Stage=IVB: 2.45%
- Response:
- 109 Response=Excellent: 53.43%
- 53 Response=Structural Incomplete: 25.98%
- 8 Response=Biochemical Incomplete: 3.92%
- 34 Response=Indeterminate: 16.67%
- Thyroid_Function:
- 3 categorical predictors were excluded from the dataset after having been observed with extremely low variance containing categories with very few or almost no variations across observations that may have limited predictive power or drive increased model complexity without performance gains:
- Hx_Smoking:
- 193 Hx_Smoking=No: 94.61%
- 11 Hx_Smoking=Yes: 5.39%
- Hx_Radiotherapy:
- 202 Hx_Radiotherapy=No: 99.02%
- 2 Hx_Radiotherapy=Yes: 0.98%
- M:
- 194 M=M0: 95.10%
- 10 M=M1: 4.90%
- Hx_Smoking:
In [81]:
##################################
# Segregating the target
# and predictor variables
##################################
thyroid_cancer_train_predictors = thyroid_cancer_train.iloc[:,:-1].columns
thyroid_cancer_train_predictors_numeric = thyroid_cancer_train.iloc[:,:-1].loc[:, thyroid_cancer_train.iloc[:,:-1].columns == 'Age'].columns
thyroid_cancer_train_predictors_categorical = thyroid_cancer_train.iloc[:,:-1].loc[:,thyroid_cancer_train.iloc[:,:-1].columns != 'Age'].columns
In [82]:
##################################
# Gathering the variable names for each numeric column
##################################
numeric_variable_name_list = thyroid_cancer_train_predictors_numeric
In [83]:
##################################
# Segregating the target variable
# and numeric predictors
##################################
histogram_grouping_variable = 'Recurred'
histogram_frequency_variable = numeric_variable_name_list.values[0]
In [84]:
##################################
# Comparing the numeric predictors
# grouped by the target variable
##################################
colors = plt.get_cmap('tab10').colors
plt.figure(figsize=(7, 5))
group_no = thyroid_cancer_train[thyroid_cancer_train[histogram_grouping_variable] == 'No'][histogram_frequency_variable]
group_yes = thyroid_cancer_train[thyroid_cancer_train[histogram_grouping_variable] == 'Yes'][histogram_frequency_variable]
plt.hist(group_no, bins=20, alpha=0.5, color=colors[0], label='No', edgecolor='black')
plt.hist(group_yes, bins=20, alpha=0.5, color=colors[1], label='Yes', edgecolor='black')
plt.title(f'{histogram_grouping_variable} Versus {histogram_frequency_variable}')
plt.xlabel(histogram_frequency_variable)
plt.ylabel('Frequency')
plt.legend()
plt.show()
In [85]:
##################################
# Performing a general exploration of the categorical variable levels
# based on the ordered categories
##################################
ordered_cat_cols = thyroid_cancer_train.select_dtypes(include=["category"]).columns
for col in ordered_cat_cols:
print(f"Column: {col}")
print("Absolute Frequencies:")
print(thyroid_cancer_train[col].value_counts().reindex(thyroid_cancer_train[col].cat.categories))
print("\nNormalized Frequencies:")
print(thyroid_cancer_train[col].value_counts(normalize=True).reindex(thyroid_cancer_train[col].cat.categories))
print("-" * 50)
Column: Gender Absolute Frequencies: M 44 F 160 Name: count, dtype: int64 Normalized Frequencies: M 0.215686 F 0.784314 Name: proportion, dtype: float64 -------------------------------------------------- Column: Smoking Absolute Frequencies: No 177 Yes 27 Name: count, dtype: int64 Normalized Frequencies: No 0.867647 Yes 0.132353 Name: proportion, dtype: float64 -------------------------------------------------- Column: Hx_Smoking Absolute Frequencies: No 193 Yes 11 Name: count, dtype: int64 Normalized Frequencies: No 0.946078 Yes 0.053922 Name: proportion, dtype: float64 -------------------------------------------------- Column: Hx_Radiotherapy Absolute Frequencies: No 202 Yes 2 Name: count, dtype: int64 Normalized Frequencies: No 0.990196 Yes 0.009804 Name: proportion, dtype: float64 -------------------------------------------------- Column: Thyroid_Function Absolute Frequencies: Euthyroid 171 Subclinical Hypothyroidism 10 Subclinical Hyperthyroidism 3 Clinical Hypothyroidism 7 Clinical Hyperthyroidism 13 Name: count, dtype: int64 Normalized Frequencies: Euthyroid 0.838235 Subclinical Hypothyroidism 0.049020 Subclinical Hyperthyroidism 0.014706 Clinical Hypothyroidism 0.034314 Clinical Hyperthyroidism 0.063725 Name: proportion, dtype: float64 -------------------------------------------------- Column: Physical_Examination Absolute Frequencies: Normal 4 Single nodular goiter-left 50 Single nodular goiter-right 68 Multinodular goiter 79 Diffuse goiter 3 Name: count, dtype: int64 Normalized Frequencies: Normal 0.019608 Single nodular goiter-left 0.245098 Single nodular goiter-right 0.333333 Multinodular goiter 0.387255 Diffuse goiter 0.014706 Name: proportion, dtype: float64 -------------------------------------------------- Column: Adenopathy Absolute Frequencies: No 144 Left 14 Right 21 Bilateral 19 Posterior 2 Extensive 4 Name: count, dtype: int64 Normalized Frequencies: No 0.705882 Left 0.068627 Right 0.102941 Bilateral 0.093137 Posterior 0.009804 Extensive 0.019608 Name: proportion, dtype: float64 -------------------------------------------------- Column: Pathology Absolute Frequencies: Hurthle Cell 15 Follicular 14 Micropapillary 26 Papillary 149 Name: count, dtype: int64 Normalized Frequencies: Hurthle Cell 0.073529 Follicular 0.068627 Micropapillary 0.127451 Papillary 0.730392 Name: proportion, dtype: float64 -------------------------------------------------- Column: Focality Absolute Frequencies: Uni-Focal 129 Multi-Focal 75 Name: count, dtype: int64 Normalized Frequencies: Uni-Focal 0.632353 Multi-Focal 0.367647 Name: proportion, dtype: float64 -------------------------------------------------- Column: Risk Absolute Frequencies: Low 127 Intermediate 60 High 17 Name: count, dtype: int64 Normalized Frequencies: Low 0.622549 Intermediate 0.294118 High 0.083333 Name: proportion, dtype: float64 -------------------------------------------------- Column: T Absolute Frequencies: T1a 26 T1b 21 T2 73 T3a 58 T3b 10 T4a 12 T4b 4 Name: count, dtype: int64 Normalized Frequencies: T1a 0.127451 T1b 0.102941 T2 0.357843 T3a 0.284314 T3b 0.049020 T4a 0.058824 T4b 0.019608 Name: proportion, dtype: float64 -------------------------------------------------- Column: N Absolute Frequencies: N0 139 N1a 11 N1b 54 Name: count, dtype: int64 Normalized Frequencies: N0 0.681373 N1a 0.053922 N1b 0.264706 Name: proportion, dtype: float64 -------------------------------------------------- Column: M Absolute Frequencies: M0 194 M1 10 Name: count, dtype: int64 Normalized Frequencies: M0 0.95098 M1 0.04902 Name: proportion, dtype: float64 -------------------------------------------------- Column: Stage Absolute Frequencies: I 174 II 21 III 2 IVA 2 IVB 5 Name: count, dtype: int64 Normalized Frequencies: I 0.852941 II 0.102941 III 0.009804 IVA 0.009804 IVB 0.024510 Name: proportion, dtype: float64 -------------------------------------------------- Column: Response Absolute Frequencies: Excellent 109 Structural Incomplete 53 Biochemical Incomplete 8 Indeterminate 34 Name: count, dtype: int64 Normalized Frequencies: Excellent 0.534314 Structural Incomplete 0.259804 Biochemical Incomplete 0.039216 Indeterminate 0.166667 Name: proportion, dtype: float64 -------------------------------------------------- Column: Recurred Absolute Frequencies: No 143 Yes 61 Name: count, dtype: int64 Normalized Frequencies: No 0.70098 Yes 0.29902 Name: proportion, dtype: float64 --------------------------------------------------
In [86]:
##################################
# Segregating the target variable
# and categorical predictors
##################################
proportion_y_variables = thyroid_cancer_train_predictors_categorical
proportion_x_variable = 'Recurred'
In [87]:
##################################
# Defining the number of
# rows and columns for the subplots
##################################
num_rows = 5
num_cols = 3
##################################
# Formulating the subplot structure
##################################
fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 25))
##################################
# Flattening the multi-row and
# multi-column axes
##################################
axes = axes.ravel()
##################################
# Formulating the individual stacked column plots
# for all categorical columns
##################################
for i, y_variable in enumerate(proportion_y_variables):
ax = axes[i]
category_counts = thyroid_cancer_train.groupby([proportion_x_variable, y_variable], observed=True).size().unstack(fill_value=0)
category_proportions = category_counts.div(category_counts.sum(axis=1), axis=0)
category_proportions.plot(kind='bar', stacked=True, ax=ax)
ax.set_title(f'{proportion_x_variable} Versus {y_variable}')
ax.set_xlabel(proportion_x_variable)
ax.set_ylabel('Proportions')
ax.legend(loc="lower center")
##################################
# Adjusting the subplot layout
##################################
plt.tight_layout()
##################################
# Presenting the subplots
##################################
plt.show()
In [88]:
##################################
# Removing predictors observed with extreme
# near-zero variance and a limited number of levels
##################################
thyroid_cancer_train_column_filtered = thyroid_cancer_train.drop(columns=['Hx_Radiotherapy','M','Hx_Smoking'])
thyroid_cancer_train_column_filtered.head()
Out[88]:
| Age | Gender | Smoking | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | 28.0 | F | No | Euthyroid | Multinodular goiter | No | Papillary | Uni-Focal | Low | T2 | N0 | I | Excellent | No |
| 205 | 36.0 | F | No | Euthyroid | Single nodular goiter-right | Right | Papillary | Uni-Focal | Low | T2 | N1b | I | Indeterminate | No |
| 277 | 41.0 | M | Yes | Euthyroid | Single nodular goiter-right | No | Hurthle Cell | Multi-Focal | Intermediate | T3a | N0 | I | Excellent | No |
| 294 | 42.0 | M | No | Subclinical Hypothyroidism | Single nodular goiter-right | No | Papillary | Multi-Focal | Intermediate | T3a | N1a | I | Indeterminate | No |
| 268 | 32.0 | F | No | Euthyroid | Single nodular goiter-left | No | Papillary | Uni-Focal | Low | T3a | N0 | I | Excellent | No |
1.4.3 Category Aggregation and Encoding¶
- Category aggregation was applied to the previously identified categorical predictors observed with many levels (high-cardinality) containing only a few observations to improve model stability during cross-validation and enhance generalization:
- Thyroid_Function:
- 171 Thyroid_Function=Euthyroid: 83.82%
- 33 Thyroid_Function=Hypothyroidism or Hyperthyroidism: 16.18%
- Physical_Examination:
- 122 Physical_Examination=Normal or Single Nodular Goiter : 59.80%
- 82 Physical_Examination=Multinodular or Diffuse Goiter: 40.20%
- Adenopathy:
- 144 Adenopathy=No: 70.59%
- 60 Adenopathy=Yes: 29.41%
- Pathology:
- 29 Pathology=Non-Papillary : 14.22%
- 175 Pathology=Papillary: 85.78%
- Risk:
- 127 Risk=Low: 62.25%
- 77 Risk=Intermediate to High: 37.75%
- T:
- 120 T=T1 to T2: 58.82%
- 84 T=T3 to T4b: 41.18%
- N:
- 139 N=N0: 68.14%
- 65 N=N1: 31.86%
- Stage:
- 174 Stage=I: 85.29%
- 30 Stage=II to IVB: 14.71%
- Response:
- 109 Response=Excellent: 53.43%
- 95 Response=Indeterminate or Incomplete: 46.57%
- Thyroid_Function:
In [89]:
##################################
# Merging small categories into broader groups
# for certain categorical predictors
# to ensure sufficient representation in statistical models
# and prevent sparsity issues in cross-validation
##################################
thyroid_cancer_train_column_filtered['Thyroid_Function'] = thyroid_cancer_train_column_filtered['Thyroid_Function'].map(lambda x: 'Euthyroid' if (x in ['Euthyroid']) else 'Hypothyroidism or Hyperthyroidism').astype('category')
thyroid_cancer_train_column_filtered['Physical_Examination'] = thyroid_cancer_train_column_filtered['Physical_Examination'].map(lambda x: 'Normal or Single Nodular Goiter' if (x in ['Normal', 'Single nodular goiter-left', 'Single nodular goiter-right']) else 'Multinodular or Diffuse Goiter').astype('category')
thyroid_cancer_train_column_filtered['Adenopathy'] = thyroid_cancer_train_column_filtered['Adenopathy'].map(lambda x: 'No' if x == 'No' else ('Yes' if pd.notna(x) and x != '' else x)).astype('category')
thyroid_cancer_train_column_filtered['Pathology'] = thyroid_cancer_train_column_filtered['Pathology'].map(lambda x: 'Non-Papillary' if (x in ['Hurthle Cell', 'Follicular']) else 'Papillary').astype('category')
thyroid_cancer_train_column_filtered['Risk'] = thyroid_cancer_train_column_filtered['Risk'].map(lambda x: 'Low' if (x in ['Low']) else 'Intermediate to High').astype('category')
thyroid_cancer_train_column_filtered['T'] = thyroid_cancer_train_column_filtered['T'].map(lambda x: 'T1 to T2' if (x in ['T1a', 'T1b', 'T2']) else 'T3 to T4b').astype('category')
thyroid_cancer_train_column_filtered['N'] = thyroid_cancer_train_column_filtered['N'].map(lambda x: 'N0' if (x in ['N0']) else 'N1').astype('category')
thyroid_cancer_train_column_filtered['Stage'] = thyroid_cancer_train_column_filtered['Stage'].map(lambda x: 'I' if (x in ['I']) else 'II to IVB').astype('category')
thyroid_cancer_train_column_filtered['Response'] = thyroid_cancer_train_column_filtered['Response'].map(lambda x: 'Indeterminate or Incomplete' if (x in ['Indeterminate', 'Structural Incomplete', 'Biochemical Incomplete']) else 'Excellent').astype('category')
thyroid_cancer_train_column_filtered.head()
Out[89]:
| Age | Gender | Smoking | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | 28.0 | F | No | Euthyroid | Multinodular or Diffuse Goiter | No | Papillary | Uni-Focal | Low | T1 to T2 | N0 | I | Excellent | No |
| 205 | 36.0 | F | No | Euthyroid | Normal or Single Nodular Goiter | Yes | Papillary | Uni-Focal | Low | T1 to T2 | N1 | I | Indeterminate or Incomplete | No |
| 277 | 41.0 | M | Yes | Euthyroid | Normal or Single Nodular Goiter | No | Non-Papillary | Multi-Focal | Intermediate to High | T3 to T4b | N0 | I | Excellent | No |
| 294 | 42.0 | M | No | Hypothyroidism or Hyperthyroidism | Normal or Single Nodular Goiter | No | Papillary | Multi-Focal | Intermediate to High | T3 to T4b | N1 | I | Indeterminate or Incomplete | No |
| 268 | 32.0 | F | No | Euthyroid | Normal or Single Nodular Goiter | No | Papillary | Uni-Focal | Low | T3 to T4b | N0 | I | Excellent | No |
In [90]:
##################################
# Performing a general exploration of the categorical variable levels
# based on the ordered categories
##################################
ordered_cat_cols = thyroid_cancer_train_column_filtered.select_dtypes(include=["category"]).columns
for col in ordered_cat_cols:
print(f"Column: {col}")
print("Absolute Frequencies:")
print(thyroid_cancer_train_column_filtered[col].value_counts().reindex(thyroid_cancer_train_column_filtered[col].cat.categories))
print("\nNormalized Frequencies:")
print(thyroid_cancer_train_column_filtered[col].value_counts(normalize=True).reindex(thyroid_cancer_train_column_filtered[col].cat.categories))
print("-" * 50)
Column: Gender Absolute Frequencies: M 44 F 160 Name: count, dtype: int64 Normalized Frequencies: M 0.215686 F 0.784314 Name: proportion, dtype: float64 -------------------------------------------------- Column: Smoking Absolute Frequencies: No 177 Yes 27 Name: count, dtype: int64 Normalized Frequencies: No 0.867647 Yes 0.132353 Name: proportion, dtype: float64 -------------------------------------------------- Column: Thyroid_Function Absolute Frequencies: Euthyroid 171 Hypothyroidism or Hyperthyroidism 33 Name: count, dtype: int64 Normalized Frequencies: Euthyroid 0.838235 Hypothyroidism or Hyperthyroidism 0.161765 Name: proportion, dtype: float64 -------------------------------------------------- Column: Physical_Examination Absolute Frequencies: Multinodular or Diffuse Goiter 82 Normal or Single Nodular Goiter 122 Name: count, dtype: int64 Normalized Frequencies: Multinodular or Diffuse Goiter 0.401961 Normal or Single Nodular Goiter 0.598039 Name: proportion, dtype: float64 -------------------------------------------------- Column: Adenopathy Absolute Frequencies: No 144 Yes 60 Name: count, dtype: int64 Normalized Frequencies: No 0.705882 Yes 0.294118 Name: proportion, dtype: float64 -------------------------------------------------- Column: Pathology Absolute Frequencies: Non-Papillary 29 Papillary 175 Name: count, dtype: int64 Normalized Frequencies: Non-Papillary 0.142157 Papillary 0.857843 Name: proportion, dtype: float64 -------------------------------------------------- Column: Focality Absolute Frequencies: Uni-Focal 129 Multi-Focal 75 Name: count, dtype: int64 Normalized Frequencies: Uni-Focal 0.632353 Multi-Focal 0.367647 Name: proportion, dtype: float64 -------------------------------------------------- Column: Risk Absolute Frequencies: Intermediate to High 77 Low 127 Name: count, dtype: int64 Normalized Frequencies: Intermediate to High 0.377451 Low 0.622549 Name: proportion, dtype: float64 -------------------------------------------------- Column: T Absolute Frequencies: T1 to T2 120 T3 to T4b 84 Name: count, dtype: int64 Normalized Frequencies: T1 to T2 0.588235 T3 to T4b 0.411765 Name: proportion, dtype: float64 -------------------------------------------------- Column: N Absolute Frequencies: N0 139 N1 65 Name: count, dtype: int64 Normalized Frequencies: N0 0.681373 N1 0.318627 Name: proportion, dtype: float64 -------------------------------------------------- Column: Stage Absolute Frequencies: I 174 II to IVB 30 Name: count, dtype: int64 Normalized Frequencies: I 0.852941 II to IVB 0.147059 Name: proportion, dtype: float64 -------------------------------------------------- Column: Response Absolute Frequencies: Excellent 109 Indeterminate or Incomplete 95 Name: count, dtype: int64 Normalized Frequencies: Excellent 0.534314 Indeterminate or Incomplete 0.465686 Name: proportion, dtype: float64 -------------------------------------------------- Column: Recurred Absolute Frequencies: No 143 Yes 61 Name: count, dtype: int64 Normalized Frequencies: No 0.70098 Yes 0.29902 Name: proportion, dtype: float64 --------------------------------------------------
In [91]:
##################################
# Segregating the target
# and predictor variables
##################################
thyroid_cancer_train_predictors = thyroid_cancer_train_column_filtered.iloc[:,:-1].columns
thyroid_cancer_train_predictors_numeric = thyroid_cancer_train_column_filtered.iloc[:,:-1].loc[:, thyroid_cancer_train_column_filtered.iloc[:,:-1].columns == 'Age'].columns
thyroid_cancer_train_predictors_categorical = thyroid_cancer_train_column_filtered.iloc[:,:-1].loc[:,thyroid_cancer_train_column_filtered.iloc[:,:-1].columns != 'Age'].columns
In [92]:
##################################
# Segregating the target variable
# and categorical predictors
##################################
proportion_y_variables = thyroid_cancer_train_predictors_categorical
proportion_x_variable = 'Recurred'
In [93]:
##################################
# Defining the number of
# rows and columns for the subplots
##################################
num_rows = 4
num_cols = 3
##################################
# Formulating the subplot structure
##################################
fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 20))
##################################
# Flattening the multi-row and
# multi-column axes
##################################
axes = axes.ravel()
##################################
# Formulating the individual stacked column plots
# for all categorical columns
##################################
for i, y_variable in enumerate(proportion_y_variables):
ax = axes[i]
category_counts = thyroid_cancer_train_column_filtered.groupby([proportion_x_variable, y_variable], observed=True).size().unstack(fill_value=0)
category_proportions = category_counts.div(category_counts.sum(axis=1), axis=0)
category_proportions.plot(kind='bar', stacked=True, ax=ax)
ax.set_title(f'{proportion_x_variable} Versus {y_variable}')
ax.set_xlabel(proportion_x_variable)
ax.set_ylabel('Proportions')
ax.legend(loc="lower center")
##################################
# Adjusting the subplot layout
##################################
plt.tight_layout()
##################################
# Presenting the subplots
##################################
plt.show()
1.4.4 Outlier and Distributional Shape Analysis¶
- No outliers (Outlier.Count>0, Outlier.Ratio>0.000), high skewness (Skewness>3 or Skewness<(-3)) or abnormal kurtosis (Skewness>2 or Skewness<(-2)) observed for the numeric predictor.
- Age: Outlier.Count = 0, Outlier.Ratio = 0.000, Skewness = 0.525, Kurtosis = -0.494
In [94]:
##################################
# Formulating the imputed dataset
# with numeric columns only
##################################
thyroid_cancer_train_column_filtered['Age'] = pd.to_numeric(thyroid_cancer_train_column_filtered['Age'])
thyroid_cancer_train_column_filtered_numeric = thyroid_cancer_train_column_filtered.select_dtypes(include='number')
thyroid_cancer_train_column_filtered_numeric = thyroid_cancer_train_column_filtered_numeric.to_frame() if isinstance(thyroid_cancer_train_column_filtered_numeric, pd.Series) else thyroid_cancer_train_column_filtered_numeric
In [95]:
##################################
# Gathering the variable names for each numeric column
##################################
numeric_variable_name_list = list(thyroid_cancer_train_column_filtered_numeric.columns)
In [96]:
##################################
# Gathering the skewness value for each numeric column
##################################
numeric_skewness_list = thyroid_cancer_train_column_filtered_numeric.skew()
In [97]:
##################################
# Computing the interquartile range
# for all columns
##################################
thyroid_cancer_train_column_filtered_numeric_q1 = thyroid_cancer_train_column_filtered_numeric.quantile(0.25)
thyroid_cancer_train_column_filtered_numeric_q3 = thyroid_cancer_train_column_filtered_numeric.quantile(0.75)
thyroid_cancer_train_column_filtered_numeric_iqr = thyroid_cancer_train_column_filtered_numeric_q3 - thyroid_cancer_train_column_filtered_numeric_q1
In [98]:
##################################
# Gathering the outlier count for each numeric column
# based on the interquartile range criterion
##################################
numeric_outlier_count_list = ((thyroid_cancer_train_column_filtered_numeric < (thyroid_cancer_train_column_filtered_numeric_q1 - 1.5 * thyroid_cancer_train_column_filtered_numeric_iqr)) | (thyroid_cancer_train_column_filtered_numeric > (thyroid_cancer_train_column_filtered_numeric_q3 + 1.5 * thyroid_cancer_train_column_filtered_numeric_iqr))).sum()
In [99]:
##################################
# Gathering the number of observations for each column
##################################
numeric_row_count_list = list([len(thyroid_cancer_train_column_filtered_numeric)] * len(thyroid_cancer_train_column_filtered_numeric.columns))
In [100]:
##################################
# Gathering the unique to count ratio for each numeric column
##################################
numeric_outlier_ratio_list = map(truediv, numeric_outlier_count_list, numeric_row_count_list)
In [101]:
##################################
# Gathering the skewness value for each numeric column
##################################
numeric_skewness_list = thyroid_cancer_train_column_filtered_numeric.skew()
In [102]:
##################################
# Gathering the kurtosis value for each numeric column
##################################
numeric_kurtosis_list = thyroid_cancer_train_column_filtered_numeric.kurtosis()
In [103]:
##################################
# Formulating the outlier summary
# for all numeric columns
##################################
numeric_column_outlier_summary = pd.DataFrame(zip(numeric_variable_name_list,
numeric_skewness_list,
numeric_outlier_count_list,
numeric_row_count_list,
numeric_outlier_ratio_list,
numeric_skewness_list,
numeric_kurtosis_list),
columns=['Numeric.Column.Name',
'Skewness',
'Outlier.Count',
'Row.Count',
'Outlier.Ratio',
'Skewness',
'Kurtosis'])
display(numeric_column_outlier_summary)
| Numeric.Column.Name | Skewness | Outlier.Count | Row.Count | Outlier.Ratio | Skewness | Kurtosis | |
|---|---|---|---|---|---|---|---|
| 0 | Age | 0.525218 | 0 | 204 | 0.0 | 0.525218 | -0.494286 |
In [104]:
##################################
# Formulating the individual boxplots
# for all numeric columns
##################################
for column in thyroid_cancer_train_column_filtered_numeric:
plt.figure(figsize=(17,1))
sns.boxplot(data=thyroid_cancer_train_column_filtered_numeric, x=column)
1.4.5 Collinearity¶
- Majority of the predictors reported low (<0.50) to moderate (0.50 to 0.75) correlation.
- Among pairwise combinations of categorical predictors, high Phi.Coefficient values were noted for:
- N and Adenopathy: Phi.Coefficient = +0.805
- N and Risk: Phi.Coefficient = +0.726
- Adenopathy and Risk: Phi.Coefficient = +0.674
In [105]:
##################################
# Creating a dataset copy and
# converting all values to numeric
# for correlation analysis
##################################
pd.set_option('future.no_silent_downcasting', True)
thyroid_cancer_train_correlation = thyroid_cancer_train_column_filtered.copy()
thyroid_cancer_train_correlation_object = thyroid_cancer_train_correlation.iloc[:,1:13].columns
custom_category_orders = {
'Gender': ['M', 'F'],
'Smoking': ['No', 'Yes'],
'Thyroid_Function': ['Euthyroid', 'Hypothyroidism or Hyperthyroidism'],
'Physical_Examination': ['Normal or Single Nodular Goiter', 'Multinodular or Diffuse Goiter'],
'Adenopathy': ['No', 'Yes'],
'Pathology': ['Non-Papillary', 'Papillary'],
'Focality': ['Uni-Focal', 'Multi-Focal'],
'Risk': ['Low', 'Intermediate to High'],
'T': ['T1 to T2', 'T3 to T4b'],
'N': ['N0', 'N1'],
'Stage': ['I', 'II to IVB'],
'Response': ['Excellent', 'Indeterminate or Incomplete']
}
encoder = OrdinalEncoder(categories=[custom_category_orders[col] for col in thyroid_cancer_train_correlation_object])
thyroid_cancer_train_correlation[thyroid_cancer_train_correlation_object] = encoder.fit_transform(
thyroid_cancer_train_correlation[thyroid_cancer_train_correlation_object]
)
thyroid_cancer_train_correlation = thyroid_cancer_train_correlation.drop(['Recurred'], axis=1)
display(thyroid_cancer_train_correlation)
| Age | Gender | Smoking | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | Stage | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | 28.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 205 | 36.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 277 | 41.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 294 | 42.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 |
| 268 | 32.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 300 | 67.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0 |
| 115 | 37.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 67 | 51.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 161 | 22.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 55 | 21.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
204 rows × 13 columns
In [106]:
##################################
# Initializing the correlation matrix
##################################
thyroid_cancer_train_correlation_matrix = pd.DataFrame(np.zeros((len(thyroid_cancer_train_correlation.columns), len(thyroid_cancer_train_correlation.columns))),
columns=thyroid_cancer_train_correlation.columns,
index=thyroid_cancer_train_correlation.columns)
In [107]:
##################################
# Creating an empty correlation matrix
##################################
thyroid_cancer_train_correlation_matrix = pd.DataFrame(
np.zeros((len(thyroid_cancer_train_correlation.columns), len(thyroid_cancer_train_correlation.columns))),
index=thyroid_cancer_train_correlation.columns,
columns=thyroid_cancer_train_correlation.columns
)
##################################
# Calculating different types
# of correlation coefficients
# per variable type
##################################
for i in range(len(thyroid_cancer_train_correlation.columns)):
for j in range(i, len(thyroid_cancer_train_correlation.columns)):
if i == j:
thyroid_cancer_train_correlation_matrix.iloc[i, j] = 1.0
else:
col_i = thyroid_cancer_train_correlation.iloc[:, i]
col_j = thyroid_cancer_train_correlation.iloc[:, j]
# Detecting binary variables (assumes binary variables are coded as 0/1)
is_binary_i = col_i.nunique() == 2
is_binary_j = col_j.nunique() == 2
# Computing the Pearson correlation for two continuous variables
if col_i.dtype in ['int64', 'float64'] and col_j.dtype in ['int64', 'float64']:
corr = col_i.corr(col_j)
# Computing the Point-Biserial correlation for continuous and binary variables
elif (col_i.dtype in ['int64', 'float64'] and is_binary_j) or (col_j.dtype in ['int64', 'float64'] and is_binary_i):
continuous_var = col_i if col_i.dtype in ['int64', 'float64'] else col_j
binary_var = col_j if is_binary_j else col_i
# Convert binary variable to 0/1 (if not already)
binary_var = binary_var.astype('category').cat.codes
corr, _ = pointbiserialr(continuous_var, binary_var)
# Computing the Phi coefficient for two binary variables
elif is_binary_i and is_binary_j:
corr = col_i.corr(col_j)
# Computing the Cramér's V for two categorical variables (if more than 2 categories)
else:
contingency_table = pd.crosstab(col_i, col_j)
chi2, _, _, _ = chi2_contingency(contingency_table)
n = contingency_table.sum().sum()
phi2 = chi2 / n
r, k = contingency_table.shape
corr = np.sqrt(phi2 / min(k - 1, r - 1)) # Cramér's V formula
# Assigning correlation values to the matrix
thyroid_cancer_train_correlation_matrix.iloc[i, j] = corr
thyroid_cancer_train_correlation_matrix.iloc[j, i] = corr
# Displaying the correlation matrix
display(thyroid_cancer_train_correlation_matrix)
| Age | Gender | Smoking | Thyroid_Function | Physical_Examination | Adenopathy | Pathology | Focality | Risk | T | N | Stage | Response | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 1.000000 | -0.185530 | 0.299971 | 0.077845 | 0.012021 | 0.073931 | -0.215274 | 0.195272 | 0.205360 | 0.246838 | 0.013195 | 0.528144 | 0.317978 |
| Gender | -0.185530 | 1.000000 | -0.604101 | -0.093290 | -0.031935 | -0.158480 | 0.127817 | -0.218103 | -0.255507 | -0.215101 | -0.178550 | -0.219727 | -0.179431 |
| Smoking | 0.299971 | -0.604101 | 1.000000 | 0.064124 | 0.004339 | 0.192350 | -0.338086 | 0.182212 | 0.233024 | 0.231679 | 0.105463 | 0.327952 | 0.215362 |
| Thyroid_Function | 0.077845 | -0.093290 | 0.064124 | 1.000000 | 0.019964 | -0.137486 | -0.049893 | 0.051564 | -0.012519 | -0.042960 | -0.043275 | 0.080702 | -0.036498 |
| Physical_Examination | 0.012021 | -0.031935 | 0.004339 | 0.019964 | 1.000000 | 0.063246 | 0.018806 | 0.245779 | 0.166012 | 0.086039 | 0.104553 | 0.054799 | 0.116526 |
| Adenopathy | 0.073931 | -0.158480 | 0.192350 | -0.137486 | 0.063246 | 1.000000 | 0.047117 | 0.288750 | 0.673638 | 0.421762 | 0.805406 | 0.278749 | 0.518887 |
| Pathology | -0.215274 | 0.127817 | -0.338086 | -0.049893 | 0.018806 | 0.047117 | 1.000000 | -0.126299 | -0.117392 | -0.286899 | 0.157869 | -0.187683 | -0.154637 |
| Focality | 0.195272 | -0.218103 | 0.182212 | 0.051564 | 0.245779 | 0.288750 | -0.126299 | 1.000000 | 0.454926 | 0.518864 | 0.307716 | 0.372331 | 0.388741 |
| Risk | 0.205360 | -0.255507 | 0.233024 | -0.012519 | 0.166012 | 0.673638 | -0.117392 | 0.454926 | 1.000000 | 0.622459 | 0.726304 | 0.533264 | 0.631330 |
| T | 0.246838 | -0.215101 | 0.231679 | -0.042960 | 0.086039 | 0.421762 | -0.286899 | 0.518864 | 0.622459 | 1.000000 | 0.368430 | 0.468168 | 0.556742 |
| N | 0.013195 | -0.178550 | 0.105463 | -0.043275 | 0.104553 | 0.805406 | 0.157869 | 0.307716 | 0.726304 | 0.368430 | 1.000000 | 0.310156 | 0.542672 |
| Stage | 0.528144 | -0.219727 | 0.327952 | 0.080702 | 0.054799 | 0.278749 | -0.187683 | 0.372331 | 0.533264 | 0.468168 | 0.310156 | 1.000000 | 0.417025 |
| Response | 0.317978 | -0.179431 | 0.215362 | -0.036498 | 0.116526 | 0.518887 | -0.154637 | 0.388741 | 0.631330 | 0.556742 | 0.542672 | 0.417025 | 1.000000 |
In [108]:
##################################
# Plotting the correlation matrix
# for all pairwise combinations
# of numeric and categorical columns
##################################
plt.figure(figsize=(17, 8))
sns.heatmap(thyroid_cancer_train_correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.show()
1.5. Data Exploration ¶
1.5.1 Exploratory Data Analysis¶
- Bivariate analysis identified individual predictors with generally positive association to the target variable based on visual inspection.
- Higher values or higher proportions for the following predictors are associated with the Recurred=Yes category:
- Age
- Gender=M
- Smoking=Yes
- Physical_Examination=Multinodular or Diffuse Goiter
- Adenopathy=Yes
- Focality=Multi-Focal
- Risk=Intermediate to High
- T=T3 to T4b
- N=N1
- Stage=II to IVB
- Response=Indeterminate or Incomplete
- Proportions for the following predictors are not associated with the Recurred=Yes or Recurred=No categories:
- Thyroid_Function
- Pathology
In [109]:
##################################
# Segregating the target
# and predictor variables
##################################
thyroid_cancer_train_column_filtered_predictors = thyroid_cancer_train_column_filtered.iloc[:,:-1].columns
thyroid_cancer_train_column_filtered_predictors_numeric = thyroid_cancer_train_column_filtered.iloc[:,:-1].loc[:, thyroid_cancer_train_column_filtered.iloc[:,:-1].columns == 'Age'].columns
thyroid_cancer_train_column_filtered_predictors_categorical = thyroid_cancer_train_column_filtered.iloc[:,:-1].loc[:,thyroid_cancer_train_column_filtered.iloc[:,:-1].columns != 'Age'].columns
In [110]:
##################################
# Gathering the variable names for each numeric column
##################################
numeric_variable_name_list = thyroid_cancer_train_column_filtered_predictors_numeric
In [111]:
##################################
# Segregating the target variable
# and numeric predictors
##################################
boxplot_y_variable = 'Recurred'
boxplot_x_variable = numeric_variable_name_list.values[0]
In [112]:
##################################
# Evaluating the numeric predictors
# against the target variable
##################################
plt.figure(figsize=(7, 5))
plt.boxplot([group[boxplot_x_variable] for name, group in thyroid_cancer_train_column_filtered.groupby(boxplot_y_variable, observed=True)])
plt.title(f'{boxplot_y_variable} Versus {boxplot_x_variable}')
plt.xlabel(boxplot_y_variable)
plt.ylabel(boxplot_x_variable)
plt.xticks(range(1, len(thyroid_cancer_train_column_filtered[boxplot_y_variable].unique()) + 1), ['No', 'Yes'])
plt.show()
In [113]:
##################################
# Segregating the target variable
# and categorical predictors
##################################
proportion_y_variables = thyroid_cancer_train_column_filtered_predictors_categorical
proportion_x_variable = 'Recurred'
In [114]:
##################################
# Defining the number of
# rows and columns for the subplots
##################################
num_rows = 4
num_cols = 3
##################################
# Formulating the subplot structure
##################################
fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 20))
##################################
# Flattening the multi-row and
# multi-column axes
##################################
axes = axes.ravel()
##################################
# Formulating the individual stacked column plots
# for all categorical columns
##################################
for i, y_variable in enumerate(proportion_y_variables):
ax = axes[i]
category_counts = thyroid_cancer_train_column_filtered.groupby([proportion_x_variable, y_variable], observed=True).size().unstack(fill_value=0)
category_proportions = category_counts.div(category_counts.sum(axis=1), axis=0)
category_proportions.plot(kind='bar', stacked=True, ax=ax)
ax.set_title(f'{proportion_x_variable} Versus {y_variable}')
ax.set_xlabel(proportion_x_variable)
ax.set_ylabel('Proportions')
ax.legend(loc="lower center")
##################################
# Adjusting the subplot layout
##################################
plt.tight_layout()
##################################
# Presenting the subplots
##################################
plt.show()
1.5.2 Hypothesis Testing¶
- The relationship between the numeric predictor to the Recurred target variable was statistically evaluated using the following hypotheses:
- Null: Difference in the means between groups Yes and No is equal to zero
- Alternative: Difference in the means between groups Yes and No is not equal to zero
- There is sufficient evidence to conclude of a statistically significant difference between the means of the numeric measurements obtained from Yes and No groups of the Recurred target variable in 1 of 1 numeric predictor given its high t-test statistic values with reported low p-values less than the significance level of 0.05.
- Age: T.Test.Statistic=-3.791, T.Test.PValue=0.000
- The relationship between the categorical predictors to the Recurred target variable was statistically evaluated using the following hypotheses:
- Null: The categorical predictor is independent of the categorical target variable
- Alternative: The categorical predictor is dependent of the categorical target variable
- There is sufficient evidence to conclude of a statistically significant relationship between the categories of the categorical predictors and the Yes and No groups of the Recurred target variable in 9 of 12 categorical predictors given their high chisquare statistic values with reported low p-values less than the significance level of 0.05.
- Risk: ChiSquare.Test.Statistic=98.599, ChiSquare.Test.PValue=0.000
- Response: ChiSquare.Test.Statistic=90.866, ChiSquare.Test.PValue=0.000
- Adenopathy: ChiSquare.Test.Statistic=73.585, ChiSquare.Test.PValue=0.000
- N: ChiSquare.Test.Statistic=73.176, ChiSquare.Test.PValue=0.000
- T: ChiSquare.Test.Statistic=62.205, ChiSquare.Test.PValue=0.000
- Stage: ChiSquare.Test.Statistic=44.963, ChiSquare.Test.PValue=0.000
- Focality: ChiSquare.Test.Statistic=32.859, ChiSquare.Test.PValue=0.000
- Gender: ChiSquare.Test.Statistic=17.787, ChiSquare.Test.PValue=0.000
- Smoking: ChiSquare.Test.Statistic=14.460, ChiSquare.Test.PValue=0.001
- There is marginal evidence to conclude of a statistically significant relationship between the categories of the categorical predictors and the Yes and No groups of the Recurred target variable in 1 of 12 categorical predictors given its sufficiently high chisquare statistic values with reported low p-values near the significance level of 0.10.
- Physical_Examination: ChiSquare.Test.Statistic=2.413, ChiSquare.Test.PValue=0.120
In [115]:
##################################
# Computing the t-test
# statistic and p-values
# between the target variable
# and numeric predictor columns
##################################
thyroid_cancer_numeric_ttest_target = {}
thyroid_cancer_numeric = thyroid_cancer_train_column_filtered.loc[:,(thyroid_cancer_train_column_filtered.columns == 'Age') | (thyroid_cancer_train_column_filtered.columns == 'Recurred')]
thyroid_cancer_numeric_columns = thyroid_cancer_train_column_filtered_predictors_numeric
for numeric_column in thyroid_cancer_numeric_columns:
group_0 = thyroid_cancer_numeric[thyroid_cancer_numeric.loc[:,'Recurred']=='No']
group_1 = thyroid_cancer_numeric[thyroid_cancer_numeric.loc[:,'Recurred']=='Yes']
thyroid_cancer_numeric_ttest_target['Recurred_' + numeric_column] = stats.ttest_ind(
group_0[numeric_column],
group_1[numeric_column],
equal_var=True)
In [116]:
##################################
# Formulating the pairwise ttest summary
# between the target variable
# and numeric predictor columns
##################################
thyroid_cancer_numeric_summary = thyroid_cancer_numeric.from_dict(thyroid_cancer_numeric_ttest_target, orient='index')
thyroid_cancer_numeric_summary.columns = ['T.Test.Statistic', 'T.Test.PValue']
display(thyroid_cancer_numeric_summary.sort_values(by=['T.Test.PValue'], ascending=True).head(len(thyroid_cancer_train_column_filtered_predictors_numeric)))
| T.Test.Statistic | T.Test.PValue | |
|---|---|---|
| Recurred_Age | -3.747942 | 0.000233 |
In [117]:
##################################
# Computing the chisquare
# statistic and p-values
# between the target variable
# and categorical predictor columns
##################################
thyroid_cancer_categorical_chisquare_target = {}
thyroid_cancer_categorical = thyroid_cancer_train_column_filtered.loc[:,(thyroid_cancer_train_column_filtered.columns != 'Age') | (thyroid_cancer_train_column_filtered.columns == 'Recurred')]
thyroid_cancer_categorical_columns = thyroid_cancer_train_column_filtered_predictors_categorical
for categorical_column in thyroid_cancer_categorical_columns:
contingency_table = pd.crosstab(thyroid_cancer_categorical[categorical_column],
thyroid_cancer_categorical['Recurred'])
thyroid_cancer_categorical_chisquare_target['Recurred_' + categorical_column] = stats.chi2_contingency(
contingency_table)[0:2]
In [118]:
##################################
# Formulating the pairwise chisquare summary
# between the target variable
# and categorical predictor columns
##################################
thyroid_cancer_categorical_summary = thyroid_cancer_categorical.from_dict(thyroid_cancer_categorical_chisquare_target, orient='index')
thyroid_cancer_categorical_summary.columns = ['ChiSquare.Test.Statistic', 'ChiSquare.Test.PValue']
display(thyroid_cancer_categorical_summary.sort_values(by=['ChiSquare.Test.PValue'], ascending=True).head(len(thyroid_cancer_train_column_filtered_predictors_categorical)))
| ChiSquare.Test.Statistic | ChiSquare.Test.PValue | |
|---|---|---|
| Recurred_Risk | 98.599608 | 3.090804e-23 |
| Recurred_Response | 90.866461 | 1.537030e-21 |
| Recurred_Adenopathy | 73.585561 | 9.636704e-18 |
| Recurred_N | 73.176134 | 1.185810e-17 |
| Recurred_T | 62.205367 | 3.094435e-15 |
| Recurred_Stage | 44.963917 | 2.006987e-11 |
| Recurred_Focality | 32.859398 | 9.907099e-09 |
| Recurred_Gender | 17.787641 | 2.469824e-05 |
| Recurred_Smoking | 14.460357 | 1.431406e-04 |
| Recurred_Physical_Examination | 2.413115 | 1.203227e-01 |
| Recurred_Thyroid_Function | 0.966826 | 3.254729e-01 |
| Recurred_Pathology | 0.131614 | 7.167646e-01 |
1.6. Premodelling Data Preparation ¶
1.6.1 Preprocessed Data Description¶
- A total of 6 of the 16 predictors were excluded from the dataset based on the data preprocessing and exploration findings
- There were 3 categorical predictors excluded from the dataset after having been observed with extremely low variance containing categories with very few or almost no variations across observations that may have limited predictive power or drive increased model complexity without performance gains:
- Hx_Smoking:
- 193 Hx_Smoking=No: 94.61%
- 11 Hx_Smoking=Yes: 5.39%
- Hx_Radiotherapy:
- 202 Hx_Radiotherapy=No: 99.02%
- 2 Hx_Radiotherapy=Yes: 0.98%
- M:
- 194 M=M0: 95.10%
- 10 M=M1: 4.90%
- Hx_Smoking:
- There was 1 categorical predictor excluded from the dataset after having been observed with high pairwise collinearity (Phi.Coefficient>0.70) with other 2 predictors that might provide redundant information, leading to potential instability in regression models.
- N and Adenopathy: Phi.Coefficient = +0.805
- N and Risk: Phi.Coefficient = +0.726
- Another 2 categorical predictors were excluded from the dataset for not exhibiting a statistically significant association with the Yes and No groups of the Recurred target variable, indicating weak predictive value.
- Thyroid_Function: ChiSquare.Test.Statistic=0.967, ChiSquare.Test.PValue=0.325
- Pathology: ChiSquare.Test.Statistic=0.132, ChiSquare.Test.PValue=0.717
- The preprocessed train data (final) subset is comprised of:
- 204 rows (observations)
- 143 Recurred=No: 70.10%
- 61 Recurred=Yes: 29.90%
- 11 columns (variables)
- 1/11 target (categorical)
- Recurred
- 1/11 predictor (numeric)
- Age
- 9/11 predictor (categorical)
- Gender
- Smoking
- Physical_Examination
- Adenopathy
- Focality
- Risk
- T
- M
- Stage
- Response
- 1/11 target (categorical)
- 204 rows (observations)
1.6.2 Preprocessing Pipeline Development¶
- A preprocessing pipeline was formulated and applied to the train data (final), validation data and test data with the following actions:
- Excluded specified columns noted with low variance, high collinearity and weak predictive power
- Aggregated categories in multiclass categorical variables into binary levels
- Converted categorical columns to the appropriate type
- Set the order of category levels for ordinal encoding during modeling pipeline creation
In [119]:
##################################
# Formulating a preprocessing pipeline
# that removes the specified columns,
# aggregates categories in multiclass categorical variables,
# converts categorical columns to the appropriate type, and
# sets the order of category levels
##################################
def preprocess_dataset(df):
# Removing the specified columns
columns_to_remove = ['Hx_Smoking', 'Hx_Radiotherapy', 'M', 'N', 'Thyroid_Function', 'Pathology']
df = df.drop(columns=columns_to_remove)
# Applying category aggregation
df['Physical_Examination'] = df['Physical_Examination'].map(
lambda x: 'Normal or Single Nodular Goiter' if x in ['Normal', 'Single nodular goiter-left', 'Single nodular goiter-right']
else 'Multinodular or Diffuse Goiter').astype('category')
df['Adenopathy'] = df['Adenopathy'].map(
lambda x: 'No' if x == 'No' else ('Yes' if pd.notna(x) and x != '' else x)).astype('category')
df['Risk'] = df['Risk'].map(
lambda x: 'Low' if x == 'Low' else 'Intermediate to High').astype('category')
df['T'] = df['T'].map(
lambda x: 'T1 to T2' if x in ['T1a', 'T1b', 'T2'] else 'T3 to T4b').astype('category')
df['Stage'] = df['Stage'].map(
lambda x: 'I' if x == 'I' else 'II to IVB').astype('category')
df['Response'] = df['Response'].map(
lambda x: 'Indeterminate or Incomplete' if x in ['Indeterminate', 'Structural Incomplete', 'Biochemical Incomplete']
else 'Excellent').astype('category')
# Setting category levels
category_mappings = {
'Gender': ['M', 'F'],
'Smoking': ['No', 'Yes'],
'Physical_Examination': ['Normal or Single Nodular Goiter', 'Multinodular or Diffuse Goiter'],
'Adenopathy': ['No', 'Yes'],
'Focality': ['Uni-Focal', 'Multi-Focal'],
'Risk': ['Low', 'Intermediate to High'],
'T': ['T1 to T2', 'T3 to T4b'],
'Stage': ['I', 'II to IVB'],
'Response': ['Excellent', 'Indeterminate or Incomplete']
}
for col, categories in category_mappings.items():
df[col] = df[col].astype('category')
df[col] = df[col].cat.set_categories(categories, ordered=True)
return df
In [120]:
##################################
# Applying the preprocessing pipeline
# to the train data
##################################
thyroid_cancer_preprocessed_train = preprocess_dataset(thyroid_cancer_train)
X_preprocessed_train = thyroid_cancer_preprocessed_train.drop('Recurred', axis = 1)
y_preprocessed_train = thyroid_cancer_preprocessed_train['Recurred']
thyroid_cancer_preprocessed_train.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TRAIN_PATH, "thyroid_cancer_preprocessed_train.csv"), index=False)
X_preprocessed_train.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TRAIN_FEATURES_PATH, "X_preprocessed_train.csv"), index=False)
y_preprocessed_train.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TRAIN_TARGET_PATH, "y_preprocessed_train.csv"), index=False)
train_csv_path = os.path.join("..", DATASETS_PREPROCESSED_TRAIN_PATH, "thyroid_cancer_preprocessed_train.csv")
print('Final Preprocessed Train Dataset Dimensions: ')
display(X_preprocessed_train.shape)
display(y_preprocessed_train.shape)
print('Final Preprocessed Train Target Variable Breakdown: ')
display(y_preprocessed_train.value_counts())
print('Final Preprocessed Train Target Variable Proportion: ')
display(y_preprocessed_train.value_counts(normalize = True))
thyroid_cancer_preprocessed_train.head()
Final Preprocessed Train Dataset Dimensions:
(204, 10)
(204,)
Final Preprocessed Train Target Variable Breakdown:
Recurred No 143 Yes 61 Name: count, dtype: int64
Final Preprocessed Train Target Variable Proportion:
Recurred No 0.70098 Yes 0.29902 Name: proportion, dtype: float64
Out[120]:
| Age | Gender | Smoking | Physical_Examination | Adenopathy | Focality | Risk | T | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | 28.0 | F | No | Multinodular or Diffuse Goiter | No | Uni-Focal | Low | T1 to T2 | I | Excellent | No |
| 205 | 36.0 | F | No | Normal or Single Nodular Goiter | Yes | Uni-Focal | Low | T1 to T2 | I | Indeterminate or Incomplete | No |
| 277 | 41.0 | M | Yes | Normal or Single Nodular Goiter | No | Multi-Focal | Intermediate to High | T3 to T4b | I | Excellent | No |
| 294 | 42.0 | M | No | Normal or Single Nodular Goiter | No | Multi-Focal | Intermediate to High | T3 to T4b | I | Indeterminate or Incomplete | No |
| 268 | 32.0 | F | No | Normal or Single Nodular Goiter | No | Uni-Focal | Low | T3 to T4b | I | Excellent | No |
In [121]:
##################################
# Applying the preprocessing pipeline
# to the validation data
##################################
thyroid_cancer_preprocessed_validation = preprocess_dataset(thyroid_cancer_validation)
X_preprocessed_validation = thyroid_cancer_preprocessed_validation.drop('Recurred', axis = 1)
y_preprocessed_validation = thyroid_cancer_preprocessed_validation['Recurred']
thyroid_cancer_preprocessed_validation.to_csv(os.path.join("..", DATASETS_PREPROCESSED_VALIDATION_PATH, "thyroid_cancer_preprocessed_validation.csv"), index=False)
X_preprocessed_validation.to_csv(os.path.join("..", DATASETS_PREPROCESSED_VALIDATION_FEATURES_PATH, "X_preprocessed_validation.csv"), index=False)
y_preprocessed_validation.to_csv(os.path.join("..", DATASETS_PREPROCESSED_VALIDATION_TARGET_PATH, "y_preprocessed_validation.csv"), index=False)
validation_csv_path = os.path.join("..", DATASETS_PREPROCESSED_VALIDATION_PATH, "thyroid_cancer_preprocessed_validation.csv")
print('Final Preprocessed Validation Dataset Dimensions: ')
display(X_preprocessed_validation.shape)
display(y_preprocessed_validation.shape)
print('Final Preprocessed Validation Target Variable Breakdown: ')
display(y_preprocessed_validation.value_counts())
print('Final Preprocessed Validation Target Variable Proportion: ')
display(y_preprocessed_validation.value_counts(normalize = True))
thyroid_cancer_preprocessed_validation.head()
Final Preprocessed Validation Dataset Dimensions:
(69, 10)
(69,)
Final Preprocessed Validation Target Variable Breakdown:
Recurred No 49 Yes 20 Name: count, dtype: int64
Final Preprocessed Validation Target Variable Proportion:
Recurred No 0.710145 Yes 0.289855 Name: proportion, dtype: float64
Out[121]:
| Age | Gender | Smoking | Physical_Examination | Adenopathy | Focality | Risk | T | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 173 | 30.0 | F | No | Normal or Single Nodular Goiter | No | Uni-Focal | Low | T1 to T2 | I | Indeterminate or Incomplete | No |
| 164 | 29.0 | F | No | Normal or Single Nodular Goiter | No | Multi-Focal | Low | T1 to T2 | I | Excellent | No |
| 256 | 21.0 | M | Yes | Normal or Single Nodular Goiter | No | Uni-Focal | Low | T3 to T4b | I | Indeterminate or Incomplete | No |
| 348 | 58.0 | F | No | Multinodular or Diffuse Goiter | Yes | Multi-Focal | Intermediate to High | T3 to T4b | II to IVB | Indeterminate or Incomplete | Yes |
| 131 | 31.0 | F | No | Normal or Single Nodular Goiter | No | Uni-Focal | Low | T1 to T2 | I | Excellent | No |
In [122]:
##################################
# Applying the preprocessing pipeline
# to the test data
##################################
thyroid_cancer_preprocessed_test = preprocess_dataset(thyroid_cancer_test)
X_preprocessed_test = thyroid_cancer_preprocessed_test.drop('Recurred', axis = 1)
y_preprocessed_test = thyroid_cancer_preprocessed_test['Recurred']
thyroid_cancer_preprocessed_test.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TEST_PATH, "thyroid_cancer_preprocessed_test.csv"), index=False)
X_preprocessed_test.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TEST_FEATURES_PATH, "X_preprocessed_test.csv"), index=False)
y_preprocessed_test.to_csv(os.path.join("..", DATASETS_PREPROCESSED_TEST_TARGET_PATH, "y_preprocessed_test.csv"), index=False)
print('Final Preprocessed Test Dataset Dimensions: ')
display(X_preprocessed_test.shape)
display(y_preprocessed_test.shape)
print('Final Preprocessed Test Target Variable Breakdown: ')
display(y_preprocessed_test.value_counts())
print('Final Preprocessed Test Target Variable Proportion: ')
display(y_preprocessed_test.value_counts(normalize = True))
thyroid_cancer_preprocessed_test.head()
Final Preprocessed Test Dataset Dimensions:
(91, 10)
(91,)
Final Preprocessed Test Target Variable Breakdown:
Recurred No 64 Yes 27 Name: count, dtype: int64
Final Preprocessed Test Target Variable Proportion:
Recurred No 0.703297 Yes 0.296703 Name: proportion, dtype: float64
Out[122]:
| Age | Gender | Smoking | Physical_Examination | Adenopathy | Focality | Risk | T | Stage | Response | Recurred | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 345 | 25.0 | F | No | Multinodular or Diffuse Goiter | Yes | Multi-Focal | Intermediate to High | T3 to T4b | I | Indeterminate or Incomplete | Yes |
| 249 | 46.0 | F | No | Normal or Single Nodular Goiter | No | Multi-Focal | Low | T3 to T4b | I | Excellent | No |
| 83 | 40.0 | F | No | Normal or Single Nodular Goiter | No | Uni-Focal | Intermediate to High | T1 to T2 | I | Excellent | No |
| 184 | 67.0 | F | No | Normal or Single Nodular Goiter | No | Uni-Focal | Low | T1 to T2 | I | Excellent | No |
| 146 | 25.0 | F | No | Multinodular or Diffuse Goiter | No | Uni-Focal | Low | T1 to T2 | I | Indeterminate or Incomplete | No |
In [123]:
##################################
# Defining a function to compute
# model performance
##################################
def model_performance_evaluation(y_true, y_pred):
metric_name = ['Accuracy','Precision','Recall','F1','AUROC']
metric_value = [accuracy_score(y_true, y_pred),
precision_score(y_true, y_pred),
recall_score(y_true, y_pred),
f1_score(y_true, y_pred),
roc_auc_score(y_true, y_pred)]
metric_summary = pd.DataFrame(zip(metric_name, metric_value),
columns=['metric_name','metric_value'])
return(metric_summary)
In [124]:
##################################
# Defining a function to compute
# model performance
##################################
def evaluate_binary_metrics(y_true, y_pred):
acc = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
recall = recall_score(y_true, y_pred) # Sensitivity
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
specificity = tn / (tn + fp) if (tn + fp) > 0 else 0
return {'accuracy': acc, 'sensitivity': recall, 'specificity': specificity, 'f1': f1}
In [125]:
##################################
# Defining a function to
# create a hash representing a specific experiment configuration
##################################
def generate_run_hash(params: dict, mccv_seed: int) -> str:
signature = {
"params": params,
"mccv_seed": mccv_seed # ensures same splits produce same hash
}
run_string = json.dumps(signature, sort_keys=True)
return hashlib.md5(run_string.encode("utf-8")).hexdigest()
In [126]:
##################################
# Defining a function to
# add utility to search MLflow for a matching hash
##################################
def find_existing_run_by_hash(experiment_name: str, run_hash: str):
client = MlflowClient()
experiment = client.get_experiment_by_name(experiment_name)
if not experiment:
return None
runs = client.search_runs(experiment_ids=[experiment.experiment_id])
for run in runs:
if run.data.tags.get("run_hash") == run_hash:
return run.info.run_id
return None
In [127]:
##################################
# Defining a function to
# delete an existing MlFlow run before logging and
# keeping only the latest
##################################
def delete_run(run_id: str):
client = MlflowClient()
client.set_terminated(run_id, status="KILLED")
client.delete_run(run_id)
In [128]:
##################################
# Defining a function to
# delete all MLflow runs in an experiment
# in case needed
##################################
def delete_all_runs_in_experiment(experiment_name: str):
client = MlflowClient()
experiment = client.get_experiment_by_name(experiment_name)
if experiment is None:
print(f"No experiment found with name '{experiment_name}'")
return
experiment_id = experiment.experiment_id
runs = client.search_runs([experiment_id], max_results=10000)
if not runs:
print(f"No runs found in experiment '{experiment_name}'")
return
print(f"Found {len(runs)} runs in experiment '{experiment_name}'. Deleting...")
for run in runs:
run_id = run.info.run_id
client.set_terminated(run_id, status="KILLED") # Optional, but safe
client.delete_run(run_id)
print("All runs deleted.")
In [129]:
##################################
# Defining a function to
# completely remove the MLflow experiment
# including the name and all metadata
##################################
def remove_experiment_name_metadata(experiment_name: str):
client = MlflowClient()
experiment = client.get_experiment_by_name(experiment_name)
if experiment:
client.delete_experiment(experiment.experiment_id)
print(f"Deleted experiment '{experiment.name}'.")
In [130]:
##################################
# Defining a function to
# generate a confusion matrix
# and log as an MLflow artifact
##################################
def log_confusion_matrix(y_true, y_pred, labels, output_path):
cm = confusion_matrix(y_true, y_pred, labels=labels)
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.title('Confusion Matrix')
plt.tight_layout()
plt.savefig(output_path)
plt.close()
In [131]:
##################################
# Defining a function to
# generate a classification report
# and log as an MLflow artifact
##################################
def log_classification_report(y_true, y_pred, labels, output_path):
report = classification_report(y_true, y_pred, target_names=[str(l) for l in labels])
with open(output_path, 'w') as f:
f.write(report)
1.7. Ensemble Model Development, Logging and Tracking using the MLflow Framework ¶
1.7.1 Random Forest¶
Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve prediction accuracy and robustness in binary classification. Instead of relying on a single decision tree, it aggregates multiple trees, reducing overfitting and increasing generalizability. The algorithm works by training individual decision trees on bootstrapped samples of the dataset, where each tree is trained on a slightly different subset of data. Additionally, at each decision node, a random subset of features is considered for splitting, adding further diversity among the trees. The final classification is determined by majority voting across all trees. The main advantages of Random Forest include its resilience to overfitting, ability to handle high-dimensional data, and robustness against noisy data. However, it has limitations, such as higher computational cost due to multiple trees and reduced interpretability compared to a single decision tree. It can also struggle with highly imbalanced data unless additional techniques like class weighting are applied.
- The random forest model from the sklearn.ensemble Python library API was implemented. Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 4 hyperparameters for tuning:
- criterion = function to measure the quality of a split made to vary between gini and entropy
- max_depth = maximum depth of the tree made to vary between 3 and 6
- min_samples_leaf = minimum number of samples required to be at a leaf node made to vary between 5 and 10
- n_estimators = number of base estimators in the ensemble made to vary between 100 and 200
- A special hyperparameter (class_weight = balanced) was fixed to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under bagged_rf_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- criterion = entropy
- max_depth = 3
- min_samples_leaf = 5
- n_estimators = 200
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.8310
- Sensitivity = 0.8778
- Specificity = 0.8948
- Accuracy = 0.8896
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8571
- Sensitivity = 0.9000
- Specificity = 0.9183
- Accuracy = 0.9130
In [132]:
##################################
# Setting the model naming conventions
##################################
model_name = "bagged_rf"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [133]:
##################################
# Setting the MLflow experiment
# for the Random Forest model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [134]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [135]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
bagged_rf_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('bagged_rf_model', RandomForestClassifier(class_weight='balanced',
random_state=987654321))
])
In [136]:
##################################
# Defining hyperparameter grid
##################################
bagged_rf_hyperparameter_grid = {
'bagged_rf_model__criterion': ['gini', 'entropy'],
'bagged_rf_model__max_depth': [3, 6],
'bagged_rf_model__min_samples_leaf': [5, 10],
'bagged_rf_model__n_estimators': [100, 200]
}
param_combos = list(ParameterGrid(bagged_rf_hyperparameter_grid))
In [137]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [138]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [139]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [140]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the Random Forest model
##################################
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("bagged_rf_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(bagged_rf_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"RF_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=a5d432cd7e9c3b18c9027bc72eb73a11). Removing old run: 415b6628c102484486bdf968a946c6cd
Evaluating param combo 2/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=5598839645a2547b53af6e59276f6ef6). Removing old run: f7d8422d650f4730b315cde493a920f4
Evaluating param combo 3/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=956ffce5827717d1916515f23ab21a66). Removing old run: 880d6e1766b54ed5a41aa09666ecea33
Evaluating param combo 4/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=eed7147b6316d00f992ec99413e153ec). Removing old run: bde45591e9a342599dff5ea2fb2a4592
Evaluating param combo 5/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=1900e2c31ed46c7a59addf558a3f89b9). Removing old run: 1466f7804ae846e19c35dbdf03ea9ff9
Evaluating param combo 6/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=45bfa5bc1d7ae7d9757753f8f5ff108b). Removing old run: 0e8536e103f340679493dca8810822e1
Evaluating param combo 7/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=c03a488f1ead20eb6657be0995d3c22a). Removing old run: 6b45f26fe89045ed9f776c4f41fe210b
Evaluating param combo 8/16: {'bagged_rf_model__criterion': 'gini', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=0b7bcf590451eb776df6dbd593cab3ab). Removing old run: 83ecf819dfc14a00ad4faf3499189561
Evaluating param combo 9/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=925c3c35a791f723107cc08fe6e75da7). Removing old run: ad62b3a9fce343459f0a294aefe7be72
Evaluating param combo 10/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=31afe18fea6914252e1d87a0f76b9901). Removing old run: c8050178e4c341c7a73bd224f841144e
Evaluating param combo 11/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=8209f8af91543888e31ffef715fbaf28). Removing old run: 84ecbc071bd84563bab43899481a26e0
Evaluating param combo 12/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=eef7e43b885e6c1da3df21a385b913a9). Removing old run: 715765b3cacb4b268e8cbc586c99f447
Evaluating param combo 13/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=78ade1ff4986105c5c8ea5b4f258114b). Removing old run: fafb06febee14156a08facd07f575b4d
Evaluating param combo 14/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=41c726def174a1b7e7b2b48b032270b7). Removing old run: 41d835480666493396779d784d201144
Evaluating param combo 15/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 100}
Duplicate found for this combo (hash=e244553ff66fd707f7fb78e4835d5544). Removing old run: 1565b3e9624a45bc8a3365724099111a
Evaluating param combo 16/16: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 6, 'bagged_rf_model__min_samples_leaf': 10, 'bagged_rf_model__n_estimators': 200}
Duplicate found for this combo (hash=36578547d5fa720aece0885694e3d15d). Removing old run: be6bdd3b87ec4f63bc2faf39a34f8af5
In [141]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("bagged_rf_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=31afe18fea6914252e1d87a0f76b9901). Removing previous run: c05c59d92aec44639161cf45c659005c
In [142]:
##################################
# Retraining the most optimal Random Forest model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for Random Forest:", best_combo)
final_model = clone(bagged_rf_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for Random Forest: {'bagged_rf_model__criterion': 'entropy', 'bagged_rf_model__max_depth': 3, 'bagged_rf_model__min_samples_leaf': 5, 'bagged_rf_model__n_estimators': 200}
Out[142]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('bagged_rf_model',
RandomForestClassifier(class_weight='balanced',
criterion='entropy', max_depth=3,
min_samples_leaf=5, n_estimators=200,
random_state=987654321))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('bagged_rf_model',
RandomForestClassifier(class_weight='balanced',
criterion='entropy', max_depth=3,
min_samples_leaf=5, n_estimators=200,
random_state=987654321))])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
RandomForestClassifier(class_weight='balanced', criterion='entropy',
max_depth=3, min_samples_leaf=5, n_estimators=200,
random_state=987654321)In [143]:
##################################
# Evaluating the most optimal Random Forest model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [144]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
bagged_rf_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | bagged_rf_model__criterion | entropy |
| 1 | bagged_rf_model__max_depth | 3 |
| 2 | bagged_rf_model__min_samples_leaf | 5 |
| 3 | bagged_rf_model__n_estimators | 200 |
bagged_rf_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8897 | 0.9130 |
| 1 | sensitivity | 0.8779 | 0.9000 |
| 2 | specificity | 0.8949 | 0.9184 |
| 3 | f1 | 0.8310 | 0.8571 |
In [145]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [146]:
##################################
# Saving the artifacts from the Random Forest model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [147]:
##################################
# Logging the most optimal Random Forest model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"RF_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_bagged_rf")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_bagged_rf_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final RF Model Trained and Logged with Validation Set Evaluation.")
Final RF Model Trained and Logged with Validation Set Evaluation.
1.7.2 AdaBoost¶
AdaBoost (Adaptive Boosting) is a boosting technique that combines multiple weak learners — typically decision stumps (shallow trees) — to form a strong classifier. It works by iteratively training weak models, assigning higher weights to misclassified instances so that subsequent models focus on difficult cases. At each iteration, a new weak model is trained, and its predictions are combined using a weighted voting mechanism. This process continues until a stopping criterion is met, such as a predefined number of iterations or performance threshold. AdaBoost is advantageous because it improves accuracy without overfitting if regularized properly. It performs well with clean data and can transform weak classifiers into strong ones. However, it is sensitive to noisy data and outliers, as misclassified points receive higher importance, leading to potential overfitting. Additionally, training can be slow for large datasets, and performance depends on the choice of base learner, typically decision trees.
- The adaboost model from the sklearn.ensemble Python library API was implemented. Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 3 hyperparameters for tuning:
- estimator_max_depth = maximum depth of the tree made to vary between 1 and 2
- learning_rate = weight applied to each classifier at each boosting iteration made to vary between 0.01 and 0.10
- n_estimators = maximum number of estimators at which boosting is terminated made to vary between 50 and 100
- No any hyperparameter was defined in the model to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under boosted_ab_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- estimator_max_depth = 1
- learning_rate = 0.10
- n_estimators = 50
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.8403
- Sensitivity = 0.8757
- Specificity = 0.9069
- Accuracy = 0.8974
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8571
- Sensitivity = 0.9000
- Specificity = 0.9183
- Accuracy = 0.9130
In [148]:
##################################
# Setting the model naming conventions
##################################
model_name = "boosted_ab"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [149]:
##################################
# Setting the MLflow experiment
# for the AdaBoost model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [150]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [151]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
boosted_ab_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('boosted_ab_model', AdaBoostClassifier(estimator=DecisionTreeClassifier(random_state=987654321),
random_state=987654321))
])
In [152]:
##################################
# Defining hyperparameter grid
##################################
boosted_ab_hyperparameter_grid = {
'boosted_ab_model__learning_rate': [0.01, 0.10],
'boosted_ab_model__estimator__max_depth': [1, 2],
'boosted_ab_model__n_estimators': [50, 100]
}
param_combos = list(ParameterGrid(boosted_ab_hyperparameter_grid))
In [153]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [154]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [155]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [156]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the AdaBoost model
##################################
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("boosted_ab_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(boosted_ab_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"AB_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/8: {'boosted_ab_model__estimator__max_depth': 1, 'boosted_ab_model__learning_rate': 0.01, 'boosted_ab_model__n_estimators': 50}
Duplicate found for this combo (hash=adea3f30682c1a791bcc7fc7796ca262). Removing old run: e96692c507954455af6bb0be2ada51f6
Evaluating param combo 2/8: {'boosted_ab_model__estimator__max_depth': 1, 'boosted_ab_model__learning_rate': 0.01, 'boosted_ab_model__n_estimators': 100}
Duplicate found for this combo (hash=f4837ebcef67ff0f6e069c7073c2ecce). Removing old run: 6f1988b373e441729baf978b9113191f
Evaluating param combo 3/8: {'boosted_ab_model__estimator__max_depth': 1, 'boosted_ab_model__learning_rate': 0.1, 'boosted_ab_model__n_estimators': 50}
Duplicate found for this combo (hash=c02c4c97a7ec2b4be4455df6acde4afb). Removing old run: eb4e5b80f309436f8533718e09e52e26
Evaluating param combo 4/8: {'boosted_ab_model__estimator__max_depth': 1, 'boosted_ab_model__learning_rate': 0.1, 'boosted_ab_model__n_estimators': 100}
Duplicate found for this combo (hash=ce0ce0096ec2c8adf9a8eb7478b4cf63). Removing old run: 885b3cc2774b4265aa548a74599864be
Evaluating param combo 5/8: {'boosted_ab_model__estimator__max_depth': 2, 'boosted_ab_model__learning_rate': 0.01, 'boosted_ab_model__n_estimators': 50}
Duplicate found for this combo (hash=624b641891c9f4c8f74d87201a813457). Removing old run: f1ed9e5550c14683b009cf235a210d1c
Evaluating param combo 6/8: {'boosted_ab_model__estimator__max_depth': 2, 'boosted_ab_model__learning_rate': 0.01, 'boosted_ab_model__n_estimators': 100}
Duplicate found for this combo (hash=a772b2c54f162164349bb3c9f5c48c5c). Removing old run: 4c79ae42c231468c94a1d4825c33255e
Evaluating param combo 7/8: {'boosted_ab_model__estimator__max_depth': 2, 'boosted_ab_model__learning_rate': 0.1, 'boosted_ab_model__n_estimators': 50}
Duplicate found for this combo (hash=c66d31c515042c9e49ad8bc9806cf270). Removing old run: 719dba6c7c044e9f9f0a0ce2f1143b7e
Evaluating param combo 8/8: {'boosted_ab_model__estimator__max_depth': 2, 'boosted_ab_model__learning_rate': 0.1, 'boosted_ab_model__n_estimators': 100}
Duplicate found for this combo (hash=2a1fc0742630d8e650385636269ed1b8). Removing old run: 3135b8e229d0454c8f4b4b435866442c
In [157]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("boosted_ab_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=c02c4c97a7ec2b4be4455df6acde4afb). Removing previous run: 77bec323c4094576b191997a5685b0e6
In [158]:
##################################
# Retraining the most optimal AdaBoost model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for AdaBoost:", best_combo)
final_model = clone(boosted_ab_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for AdaBoost: {'boosted_ab_model__estimator__max_depth': 1, 'boosted_ab_model__learning_rate': 0.1, 'boosted_ab_model__n_estimators': 50}
Out[158]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_ab_model',
AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1,
random_state=987654321),
learning_rate=0.1,
random_state=987654321))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_ab_model',
AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1,
random_state=987654321),
learning_rate=0.1,
random_state=987654321))])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
AdaBoostClassifier(estimator=DecisionTreeClassifier(max_depth=1,
random_state=987654321),
learning_rate=0.1, random_state=987654321)DecisionTreeClassifier(max_depth=1, random_state=987654321)
DecisionTreeClassifier(max_depth=1, random_state=987654321)
In [159]:
##################################
# Evaluating the most optimal AdaBoost model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [160]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
boosted_ab_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | boosted_ab_model__estimator__max_depth | 1.0 |
| 1 | boosted_ab_model__learning_rate | 0.1 |
| 2 | boosted_ab_model__n_estimators | 50.0 |
boosted_ab_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8974 | 0.9130 |
| 1 | sensitivity | 0.8758 | 0.9000 |
| 2 | specificity | 0.9070 | 0.9184 |
| 3 | f1 | 0.8404 | 0.8571 |
In [161]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [162]:
##################################
# Saving the artifacts from the AdaBoost model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [163]:
##################################
# Logging the most optimal AdaBoost model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"AB_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_boosted_ab")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_boosted_ab_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final AB Model Trained and Logged with Validation Set Evaluation.")
Final AB Model Trained and Logged with Validation Set Evaluation.
1.7.3 Gradient Boosting¶
Gradient Boosting builds an ensemble of decision trees sequentially, where each new tree corrects the mistakes of the previous ones by optimizing a loss function. Unlike AdaBoost, which reweights misclassified instances, Gradient Boosting fits each new tree to the residual errors of the previous model, gradually improving predictions. This process continues until a stopping criterion, such as a set number of trees, is met. The key advantages of Gradient Boosting include its flexibility to model complex relationships and strong predictive performance, often outperforming bagging methods. It can handle both numeric and categorical data well. However, it is prone to overfitting if not carefully tuned, especially with deep trees and too many iterations. It is also computationally expensive due to sequential training, and hyperparameter tuning (e.g., learning rate, number of trees, tree depth) can be challenging and time-consuming.
- The gradient boosting model from the sklearn.ensemble Python library API was implemented. Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 4 hyperparameters for tuning:
- learning_rate = shrinking proportion of the contribution from each tree made to vary between 0.01 and 0.10
- max_depth = maximum depth of the tree made to vary between 3 and 6
- min_samples_leaf = minimum number of samples required to be at a leaf node made to vary between 5 and 10
- n_estimators = number of boosting stages to perform made to vary between 50 and 100
- No any hyperparameter was defined in the model to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under boosted_gb_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- learning_rate = 0.10
- max_depth = 3
- min_samples_leaf = 10
- n_estimators = 50
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.7850
- Sensitivity = 0.7705
- Specificity = 0.9181
- Accuracy = 0.8729
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8292
- Sensitivity = 0.8500
- Specificity = 0.9183
- Accuracy = 0.8985
In [164]:
##################################
# Setting the model naming conventions
##################################
model_name = "boosted_gb"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [165]:
##################################
# Setting the MLflow experiment
# for the Gradient Boosting model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [166]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [167]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
boosted_gb_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('boosted_gb_model', GradientBoostingClassifier(random_state=987654321))
])
In [168]:
##################################
# Defining hyperparameter grid
##################################
boosted_gb_hyperparameter_grid = {
'boosted_gb_model__learning_rate': [0.01, 0.10],
'boosted_gb_model__max_depth': [3, 6],
'boosted_gb_model__min_samples_leaf': [5, 10],
'boosted_gb_model__n_estimators': [50, 100]
}
param_combos = list(ParameterGrid(boosted_gb_hyperparameter_grid))
In [169]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [170]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [171]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [172]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the Gradient Boosting model
##################################
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("boosted_gb_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(boosted_gb_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"GB_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=c9d3fa21df6427a54ccc941a57acd173). Removing old run: b0b920208aa544d593e4aff155d44843
Evaluating param combo 2/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=747ceea9a2b1b393e8301b3d47968555). Removing old run: 900663421a0248b88f5e589020d7e005
Evaluating param combo 3/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=1f02e54eb3b4a4b0bfc6483cb5052932). Removing old run: b21bdd6fc0444300813d309887f253b1
Evaluating param combo 4/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=12b685edf4bb7030dd7a86a0a9ff4ceb). Removing old run: cc521aa274774064b6e0ba66f5a4c1ea
Evaluating param combo 5/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=483f8b7e4542c941ab5ffee6f7fb94b6). Removing old run: 763b90ad24e942808550d2790093cec1
Evaluating param combo 6/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=b8bffc559956896b38a7575a2f468731). Removing old run: 2ce27fa4843c4852b3821c7c4cced5c6
Evaluating param combo 7/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=ab334f1aaa60442be90e6bd94f8361ea). Removing old run: a8f04a56fe5d40588fefb3c5fb1c0e75
Evaluating param combo 8/16: {'boosted_gb_model__learning_rate': 0.01, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=1e3a55a8491a0921132412cdbf74e50f). Removing old run: bd39d373a7f14596bded3cc32431d550
Evaluating param combo 9/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=6721b717a57a2bcbaaaf417ef6c4eb76). Removing old run: 422c7bc3e45c41ad8592f211b3bce3c7
Evaluating param combo 10/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=3ced802f49f4f725a1669f212d8ee972). Removing old run: 2e1d7d6d03de47ada04d733452626bab
Evaluating param combo 11/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=d574d23109eb11783038ded97ebde691). Removing old run: 075670d48fec4f06aa3d0de39fe51e75
Evaluating param combo 12/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=3d25b57cd37ca09f0549cac6fc47db53). Removing old run: 311ea452262f49a9897675cd53e708ce
Evaluating param combo 13/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=0475e97274cbb16b0a8cf69d502d3365). Removing old run: bd2d59b0e71b4603baace9548f56f6bb
Evaluating param combo 14/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 5, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=59697fe5055c3fa1484468b7605d4488). Removing old run: 935f63fdf1d24441846fe6a3d1918294
Evaluating param combo 15/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 50}
Duplicate found for this combo (hash=b68d8eedf5857028c655e52f681d763d). Removing old run: 027c51611ba74cc89b5a1fcf83cca6fc
Evaluating param combo 16/16: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 6, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 100}
Duplicate found for this combo (hash=6b840a2524b1bd5c9cfdc1b548a4c246). Removing old run: c0bbd80055ff48258b0cf88f904dcd23
In [173]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("boosted_gb_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=d574d23109eb11783038ded97ebde691). Removing previous run: 5f3b7d01ba9145a991ce85f02faef271
In [174]:
##################################
# Retraining the most optimal Gradient Boosting model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for Gradient Boosting:", best_combo)
final_model = clone(boosted_gb_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for Gradient Boosting: {'boosted_gb_model__learning_rate': 0.1, 'boosted_gb_model__max_depth': 3, 'boosted_gb_model__min_samples_leaf': 10, 'boosted_gb_model__n_estimators': 50}
Out[174]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_gb_model',
GradientBoostingClassifier(min_samples_leaf=10,
n_estimators=50,
random_state=987654321))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_gb_model',
GradientBoostingClassifier(min_samples_leaf=10,
n_estimators=50,
random_state=987654321))])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
GradientBoostingClassifier(min_samples_leaf=10, n_estimators=50,
random_state=987654321)In [175]:
##################################
# Evaluating the most optimal Gradient Boosting model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [176]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
boosted_gb_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | boosted_gb_model__learning_rate | 0.1 |
| 1 | boosted_gb_model__max_depth | 3.0 |
| 2 | boosted_gb_model__min_samples_leaf | 10.0 |
| 3 | boosted_gb_model__n_estimators | 50.0 |
boosted_gb_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8729 | 0.8986 |
| 1 | sensitivity | 0.7705 | 0.8500 |
| 2 | specificity | 0.9181 | 0.9184 |
| 3 | f1 | 0.7851 | 0.8293 |
In [177]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [178]:
##################################
# Saving the artifacts from the Gradient Boosting model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [179]:
##################################
# Logging the most optimal Gradient Boosting model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"GB_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_boosted_gb")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_boosted_gb_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final GB Model Trained and Logged with Validation Set Evaluation.")
Final GB Model Trained and Logged with Validation Set Evaluation.
1.7.4 XGBoost¶
XGBoost (Extreme Gradient Boosting) is an optimized version of Gradient Boosting that introduces additional regularization and computational efficiencies. It builds decision trees sequentially, with each new tree correcting the residual errors of the previous ones, but it incorporates advanced techniques such as shrinkage (learning rate), column subsampling, and L1/L2 regularization to prevent overfitting. Additionally, XGBoost employs parallelization, reducing training time significantly compared to standard Gradient Boosting. It is widely used in machine learning competitions due to its superior accuracy and efficiency. The key advantages include its ability to handle missing data, built-in regularization for better generalization, and fast training through parallelization. However, XGBoost requires careful hyperparameter tuning to achieve optimal performance, and the model can become overly complex, making interpretation difficult. It is also memory-intensive, especially for large datasets, and can be challenging to deploy efficiently in real-time applications.
- The xgboost model from the xgboost Python library API was implemented.Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 4 hyperparameters for tuning:
- learning_rate = step size at which weights are updated during training made to vary between 0.01 and 0.10
- max_depth = maximum depth of the tree made to vary between 3 and 6
- gamma = minimum loss reduction required to make a further split in a tree made to vary between 0.10 and 0.20
- n_estimators = number of boosting stages to perform made to vary between 50 and 100
- A special hyperparameter (scale_pos_weight = 2.0) was fixed to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under bagged_rf_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- learning_rate = 0.01
- max_depth = 3
- gamma 0.10
- n_estimators = 50
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.8264
- Sensitivity = 0.8442
- Specificity = 0.9125
- Accuracy = 0.8916
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8571
- Sensitivity = 0.9000
- Specificity = 0.9183
- Accuracy = 0.9130
In [180]:
##################################
# Setting the model naming conventions
##################################
model_name = "boosted_xgb"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [181]:
##################################
# Setting the MLflow experiment
# for the XGBoost model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [182]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [183]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
boosted_xgb_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('boosted_xgb_model', XGBClassifier(scale_pos_weight=2.0,
random_state=987654321,
subsample=0.7,
colsample_bytree=0.7,
eval_metric='logloss'))
])
In [184]:
##################################
# Defining hyperparameter grid
##################################
boosted_xgb_hyperparameter_grid = {
'boosted_xgb_model__learning_rate': [0.01, 0.10],
'boosted_xgb_model__max_depth': [3, 6],
'boosted_xgb_model__gamma': [0.1, 0.2],
'boosted_xgb_model__n_estimators': [50, 100]
}
param_combos = list(ParameterGrid(boosted_xgb_hyperparameter_grid))
In [185]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [186]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [187]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [188]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the XGBoost model
##################################
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("boosted_xgb_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(boosted_xgb_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"XGB_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=df9bf45aef69ddc89baf1f83e486d043). Removing old run: 64dfa8b4639247349d8bdfada83767a8
Evaluating param combo 2/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=58a7168720c1fd4c1b467cd3ec42014b). Removing old run: 3ab3a05f52554686a3d516313e2e83ec
Evaluating param combo 3/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=d9bd415cbfb544dc2ba3328ecc368bc2). Removing old run: 4d6c94b22b2a4e17bad137760d66b73e
Evaluating param combo 4/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=86636543044ed99f3976539cb2e7cb7a). Removing old run: cb22ad3b41d743c18a0ffddc11aee500
Evaluating param combo 5/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=ba3bd6ea2a327793b620b81909bc1fab). Removing old run: e2c668ca97bb4a1793c1e76093151226
Evaluating param combo 6/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=d0180dc353587aead8d2997540b88c2a). Removing old run: dfe0cd02405a46faa890be7ddc190c4d
Evaluating param combo 7/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=d792ba10379e693efb9832224ce91848). Removing old run: cd7726bff7e142a991b0a15925315ae9
Evaluating param combo 8/16: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=20a3678a3c262893f56cc2e33467177e). Removing old run: f9e0fe5066714ac7a8ff8a252b01c07d
Evaluating param combo 9/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=df12dfc48250c887d56fe46af95147aa). Removing old run: 5572f45ca0964c1cade460c748a252e4
Evaluating param combo 10/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=4483bdff4370a7d66aa505909511fa25). Removing old run: 3b41e30eaaa2460ea0e4ad3de1720bf6
Evaluating param combo 11/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=8a1fbd418727bc373189c75e024f3b4a). Removing old run: 383a7237599c45409f83ad3099135308
Evaluating param combo 12/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=b1cafdf9c54c1a0e355dcac35b9ab6e9). Removing old run: 62dc355a2ed447718ffbd1060a85edbd
Evaluating param combo 13/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=0b4bde63f1a9b1f6be5e869e42926d76). Removing old run: 91841c1d82b7454c9a7ad265c27860eb
Evaluating param combo 14/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=dff5b1d2039084bdb41a870901fd39e7). Removing old run: 0c5ab0c7771043d68abc594874c7b7bc
Evaluating param combo 15/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 50}
Duplicate found for this combo (hash=736a4c39a2c030bf9b7e84f2aeaaddd0). Removing old run: 3b1e346efb814a529f3ac0d8557f8dda
Evaluating param combo 16/16: {'boosted_xgb_model__gamma': 0.2, 'boosted_xgb_model__learning_rate': 0.1, 'boosted_xgb_model__max_depth': 6, 'boosted_xgb_model__n_estimators': 100}
Duplicate found for this combo (hash=ea71e5a334e7dd2bb0e17a28b5cc1ddc). Removing old run: 22933b24bf054226bef219dc2015817a
In [189]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("boosted_xgb_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=df9bf45aef69ddc89baf1f83e486d043). Removing previous run: 65b9890ade0949a994a00770df2cbccb
In [190]:
##################################
# Retraining the most optimal XGBoost model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for XGBoost:", best_combo)
final_model = clone(boosted_xgb_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for XGBoost: {'boosted_xgb_model__gamma': 0.1, 'boosted_xgb_model__learning_rate': 0.01, 'boosted_xgb_model__max_depth': 3, 'boosted_xgb_model__n_estimators': 50}
Out[190]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_xgb_model',
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_byle...
feature_types=None, feature_weights=None,
gamma=0.1, grow_policy=None,
importance_type=None,
interaction_constraints=None, learning_rate=0.01,
max_bin=None, max_cat_threshold=None,
max_cat_to_onehot=None, max_delta_step=None,
max_depth=3, max_leaves=None,
min_child_weight=None, missing=nan,
monotone_constraints=None, multi_strategy=None,
n_estimators=50, n_jobs=None,
num_parallel_tree=None, ...))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_xgb_model',
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_byle...
feature_types=None, feature_weights=None,
gamma=0.1, grow_policy=None,
importance_type=None,
interaction_constraints=None, learning_rate=0.01,
max_bin=None, max_cat_threshold=None,
max_cat_to_onehot=None, max_delta_step=None,
max_depth=3, max_leaves=None,
min_child_weight=None, missing=nan,
monotone_constraints=None, multi_strategy=None,
n_estimators=50, n_jobs=None,
num_parallel_tree=None, ...))])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=0.7, device=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric='logloss',
feature_types=None, feature_weights=None, gamma=0.1,
grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.01, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=3, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
multi_strategy=None, n_estimators=50, n_jobs=None,
num_parallel_tree=None, ...)In [191]:
##################################
# Evaluating the most optimal XGBoost model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [192]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
boosted_xgb_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | boosted_xgb_model__gamma | 0.10 |
| 1 | boosted_xgb_model__learning_rate | 0.01 |
| 2 | boosted_xgb_model__max_depth | 3.00 |
| 3 | boosted_xgb_model__n_estimators | 50.00 |
boosted_xgb_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8916 | 0.9130 |
| 1 | sensitivity | 0.8442 | 0.9000 |
| 2 | specificity | 0.9126 | 0.9184 |
| 3 | f1 | 0.8264 | 0.8571 |
In [193]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [194]:
##################################
# Saving the artifacts from the XGBoost model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [195]:
##################################
# Logging the most optimal XGBoost model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"XGB_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_boosted_xgb")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_boosted_xgb_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final XGB Model Trained and Logged with Validation Set Evaluation.")
Final XGB Model Trained and Logged with Validation Set Evaluation.
1.7.5 Light GBM¶
Light GBM (Light Gradient Boosting Machine) is a variation of Gradient Boosting designed for efficiency and scalability. Unlike traditional boosting methods that grow trees level by level, LightGBM grows trees leaf-wise, choosing the most informative splits, leading to faster convergence. It also uses histogram-based binning to speed up computations. These optimizations allow LightGBM to train on large datasets efficiently while maintaining high accuracy. Its advantages include faster training speed, reduced memory usage, and strong predictive performance, particularly for large datasets with many features. However, LightGBM can overfit more easily than XGBoost if not properly tuned, and it may not perform as well on small datasets. Additionally, its handling of categorical variables requires careful preprocessing, and the leaf-wise tree growth can sometimes lead to instability if not controlled properly.
- The light gbm model from the light Python library API was implemented. Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 4 hyperparameters for tuning:
- learning_rate = step size at which weights are updated during training made to vary between 0.01 and 0.10
- min_child_samples = minimum number of data needed in a child 3 and 6
- num_leaves = maximum tree leaves for base learners made to vary between 8 and 16
- n_estimators = number of boosted trees to fit made to vary between 50 and 100
- A special hyperparameter (scale_pos_weight = 2.0) was fixed to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under bagged_rf_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- learning_rate = 0.01
- min_child_samples = 3
- num_leaves 8
- n_estimators = 100
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.8025
- Sensitivity = 0.8000
- Specificity = 0.9162
- Accuracy = 0.8806
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8292
- Sensitivity = 0.8500
- Specificity = 0.9183
- Accuracy = 0.8985
In [196]:
##################################
# Setting the model naming conventions
##################################
model_name = "boosted_lgbm"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [197]:
##################################
# Setting the MLflow experiment
# for the Light GBM model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [198]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [199]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
boosted_lgbm_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('boosted_lgbm_model', LGBMClassifier(scale_pos_weight=2.0,
random_state=987654321,
max_depth=-1,
feature_fraction =0.7,
bagging_fraction=0.7,
verbose=-1))
])
In [200]:
##################################
# Defining hyperparameter grid
##################################
boosted_lgbm_hyperparameter_grid = {
'boosted_lgbm_model__learning_rate': [0.01, 0.10],
'boosted_lgbm_model__min_child_samples': [3, 6],
'boosted_lgbm_model__num_leaves': [8, 16],
'boosted_lgbm_model__n_estimators': [50, 100]
}
param_combos = list(ParameterGrid(boosted_lgbm_hyperparameter_grid))
In [201]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [202]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [203]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [204]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the Light GBM model
##################################
import warnings
warnings.filterwarnings('ignore', category=UserWarning, module='sklearn.utils.validation')
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("boosted_lgbm_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(boosted_lgbm_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"LGBM_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=cfbcfc69d01483171c99767848e6ff6c). Removing old run: 27d5d837c229491f81d95a2434fe7966
Evaluating param combo 2/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=365bd16ade45a726de60dbfbf3131994). Removing old run: f7445d4962064658b6f83851b9c75442
Evaluating param combo 3/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=c5ec25839801fb3bc539c05d4def3cef). Removing old run: 4a09a6afd7c34f5faea6ed021f32b06f
Evaluating param combo 4/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=7db4d8d0b67551a6de17a401d1a4275d). Removing old run: f5a19b58038b4f88845e36bfad9a7a0f
Evaluating param combo 5/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=44f22c5b7808559db6fa9f87c58f8f9f). Removing old run: b771955543e04d2c900a6e4d30d688df
Evaluating param combo 6/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=97c39002bc2d9c31c49bb580c559d1f0). Removing old run: 6846a4f9648a48aa8c514188168b39bd
Evaluating param combo 7/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=bf15c0f4bc63a1ce68fac4c0c89d897e). Removing old run: e1980d1a507144fc9e2e4a584d738ce8
Evaluating param combo 8/16: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=9dcec3542ac55d6e75728a7fac5a600a). Removing old run: 684f577596d74c32ab2d710c119d3c0b
Evaluating param combo 9/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=d7f2aee7bf3b04785c4bfd53c9fffd23). Removing old run: ff793b5faf424824a5e47cf4c1ac582b
Evaluating param combo 10/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=94f72ec03b1151dfbf497a6c7e1c6307). Removing old run: f362a665aba04fde9f6a479ea488a9bd
Evaluating param combo 11/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=b2c57e527890facb410bca76851e5cb7). Removing old run: 4f0c8095204342a088e02de0d26473ba
Evaluating param combo 12/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=eefe23e5a8f59bc89c4e737e95b13c9e). Removing old run: c461f7c7844e46d383203da2f747a5c8
Evaluating param combo 13/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=7e206eaf832238b8cc6375d8890f0311). Removing old run: 7c9fd22ba97143afad4700455f9e89e2
Evaluating param combo 14/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 50, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=1e567c4bab4eae2b6edb1a262e5895aa). Removing old run: 47cd4a69ffd2410fa335a48f74b7e907
Evaluating param combo 15/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 8}
Duplicate found for this combo (hash=1de53d811c7746ae5aae65c7ef6f0fe6). Removing old run: 21a0a92e8e144f9f98fd4bbfebef0da8
Evaluating param combo 16/16: {'boosted_lgbm_model__learning_rate': 0.1, 'boosted_lgbm_model__min_child_samples': 6, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 16}
Duplicate found for this combo (hash=70874e474e04a087b85a5babba5b7101). Removing old run: 9b67169af2c140b2953d3d51593b281f
In [205]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("boosted_lgbm_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=c5ec25839801fb3bc539c05d4def3cef). Removing previous run: 25380d292b7d44cf927a4db3101b5a11
In [206]:
##################################
# Retraining the most optimal Light GBM model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for Light GBM:", best_combo)
final_model = clone(boosted_lgbm_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for Light GBM: {'boosted_lgbm_model__learning_rate': 0.01, 'boosted_lgbm_model__min_child_samples': 3, 'boosted_lgbm_model__n_estimators': 100, 'boosted_lgbm_model__num_leaves': 8}
Out[206]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_lgbm_model',
LGBMClassifier(bagging_fraction=0.7, feature_fraction=0.7,
learning_rate=0.01, min_child_samples=3,
num_leaves=8, random_state=987654321,
scale_pos_weight=2.0, verbose=-1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_lgbm_model',
LGBMClassifier(bagging_fraction=0.7, feature_fraction=0.7,
learning_rate=0.01, min_child_samples=3,
num_leaves=8, random_state=987654321,
scale_pos_weight=2.0, verbose=-1))])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
LGBMClassifier(bagging_fraction=0.7, feature_fraction=0.7, learning_rate=0.01,
min_child_samples=3, num_leaves=8, random_state=987654321,
scale_pos_weight=2.0, verbose=-1)In [207]:
##################################
# Evaluating the most optimal Light GBM model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [208]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
boosted_lgbm_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | boosted_lgbm_model__learning_rate | 0.01 |
| 1 | boosted_lgbm_model__min_child_samples | 3.00 |
| 2 | boosted_lgbm_model__n_estimators | 100.00 |
| 3 | boosted_lgbm_model__num_leaves | 8.00 |
boosted_lgbm_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8806 | 0.8986 |
| 1 | sensitivity | 0.8000 | 0.8500 |
| 2 | specificity | 0.9163 | 0.9184 |
| 3 | f1 | 0.8025 | 0.8293 |
In [209]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [210]:
##################################
# Saving the artifacts from the Light GBM model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [211]:
##################################
# Logging the most optimal Light GBM model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"LGBM_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_boosted_lgbm")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_boosted_lgbm_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final LGBM Model Trained and Logged with Validation Set Evaluation.")
Final LGBM Model Trained and Logged with Validation Set Evaluation.
1.7.6 CatBoost¶
CatBoost (Categorical Boosting) is a boosting algorithm optimized for categorical data. Unlike other gradient boosting methods that require categorical variables to be manually encoded, CatBoost handles them natively, reducing preprocessing effort and improving performance. It builds decision trees iteratively, like other boosting methods, but uses ordered boosting to prevent target leakage and enhance generalization. The main advantages of CatBoost are its ability to handle categorical data without extensive preprocessing, high accuracy with minimal tuning, and robustness against overfitting due to built-in regularization. Additionally, it is relatively fast and memory-efficient. However, CatBoost can still be slower than LightGBM on very large datasets, and while it requires less tuning, improper parameter selection can lead to suboptimal performance. Its internal mechanics, such as ordered boosting, make interpretation more complex compared to simpler models.
- The catboost model from the catboost Python library API was implemented. Combined with a preprocessing operation into a pipeline, the mlflow.sklearn module was used for loading and logging the model during experiment tracking.
- The model contains 4 hyperparameters for tuning:
- learning_rate = step size at which weights are updated during training made to vary between 0.01 and 0.10
- max_depth = maximum depth of each decision tree in the boosting process made to vary between 3 and 6
- num_leaves = maximum tree leaves for base learners made to vary between 8 and 16
- iterations = number of boosted trees to fit made to vary between 50 and 100
- A special hyperparameter (scale_pos_weight = 2.0) was fixed to address the minimal 2:1 class imbalance observed between the No and Yes Recurred categories.
- Hyperparameter tuning was conducted using a 25-cycle 70-30 stratified train-test split Monte Carlo cross-validation with the results logged using MLflow experiment tracking and organized under bagged_rf_experiment.
- The optimal model performance evaluated using the F1 score, sensitivity and specificity metrics, respectively, was determined for the following hyperparameter combination:
- learning_rate = 0.01
- max_depth = 3
- num_leaves = 8
- iterations = 50
- The MCCV model performance of the optimal model as accessed from the MLflow user interface is summarized as follows:
- F1 Score = 0.8348
- Sensitivity = 0.8799
- Specificity = 0.8976
- Accuracy = 0.8922
- The independent validation model performance as accessed from the MLflow user interface of the optimal model is summarized as follows:
- F1 Score = 0.8571
- Sensitivity = 0.9000
- Specificity = 0.9183
- Accuracy = 0.9130
In [212]:
##################################
# Setting the model naming conventions
##################################
model_name = "boosted_cb"
experiment_name = f"{model_name}_experiment"
artifact_dir = f"artifacts/{model_name}"
os.makedirs(artifact_dir, exist_ok=True)
In [213]:
##################################
# Setting the MLflow experiment
# for the CatBoost model
##################################
mlflow.set_experiment(experiment_name)
client = MlflowClient()
In [214]:
##################################
# Defining the categorical preprocessing parameters
##################################
categorical_features = ['Gender','Smoking','Physical_Examination','Adenopathy','Focality','Risk','T','Stage','Response']
categorical_transformer = OrdinalEncoder()
categorical_preprocessor = ColumnTransformer(transformers=[
('cat', categorical_transformer, categorical_features)],
remainder='passthrough',
force_int_remainder_cols=False)
In [215]:
##################################
# Defining the preprocessing and modeling pipeline parameters
##################################
boosted_cb_pipeline = Pipeline([
('categorical_preprocessor', categorical_preprocessor),
('boosted_cb_model', CatBoostClassifier(scale_pos_weight=2.0,
random_state=987654321,
subsample =0.7,
colsample_bylevel=0.7,
grow_policy='Lossguide'))
])
In [216]:
##################################
# Defining hyperparameter grid
##################################
boosted_cb_hyperparameter_grid = {
'boosted_cb_model__learning_rate': [0.01, 0.10],
'boosted_cb_model__max_depth': [3, 6],
'boosted_cb_model__num_leaves': [8, 16],
'boosted_cb_model__iterations': [50, 100]
}
param_combos = list(ParameterGrid(boosted_cb_hyperparameter_grid))
In [217]:
##################################
# Encoding the response variables
# for model evaluation
##################################
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_validation_encoded = y_encoder.transform(y_preprocessed_validation.values.reshape(-1, 1)).ravel()
In [218]:
##################################
# Defining the cross-validation strategy
# using Monte Carlo Cross-Validation Setup (25 cycles of 70-30 stratified splits))
##################################
mccv = StratifiedShuffleSplit(n_splits=25,
test_size=0.3,
random_state=987654321)
In [219]:
##################################
# Creating containers for the best hyperparameter combination
# based on the F1 Score, Sensitivity and Specificity metrics
##################################
best_combo = None
best_metrics = {'f1': -1, 'sensitivity': -1, 'specificity': -1}
all_combos_metrics = []
In [220]:
##################################
# Implementing experiment tracking
# of the hyperparameter tuning exercise
# using MLflow for the CatBoost model
##################################
for combo_index, param_set in enumerate(param_combos):
print(f"Evaluating param combo {combo_index + 1}/{len(param_combos)}: {param_set}")
# Generating hash and checking for duplicates
run_hash = generate_run_hash(param_set, mccv_seed=987654321)
existing_run_id = find_existing_run_by_hash("boosted_cb_experiment", run_hash)
if existing_run_id:
print(f"Duplicate found for this combo (hash={run_hash}). Removing old run: {existing_run_id}")
delete_run(existing_run_id)
else:
print(f"No duplicate found. Proceeding to evaluate and log.")
all_metrics = []
for i, (train_idx, test_idx) in enumerate(mccv.split(X_preprocessed_train, y_preprocessed_train_encoded)):
X_train, X_test = X_preprocessed_train.iloc[train_idx], X_preprocessed_train.iloc[test_idx]
y_train, y_test = y_preprocessed_train_encoded[train_idx], y_preprocessed_train_encoded[test_idx]
# Cloning the pipeline and setting the hyperparameters
model = clone(boosted_cb_pipeline)
model.set_params(**param_set)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
all_metrics.append(evaluate_binary_metrics(y_test, y_pred))
# Aggregating the metrics across MCCV
df_metrics = pd.DataFrame(all_metrics)
avg_metrics = df_metrics.mean().to_dict()
avg_metrics_prefixed = {f"avg_mccv_{k}": v for k, v in avg_metrics.items()}
all_combos_metrics.append({**param_set, **avg_metrics_prefixed})
# Logging the hyperparameter combinations and metrics to MLflow
with mlflow.start_run(run_name=f"CB_HyperparameterCombo_{combo_index+1}"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", run_hash)
mlflow.log_params(param_set)
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in avg_metrics.items()})
mlflow.set_tag("param_index", combo_index)
# Tracking the best hyperparameter combination
better = False
if avg_metrics['f1'] > best_metrics['f1']:
better = True
elif avg_metrics['f1'] == best_metrics['f1']:
if avg_metrics['sensitivity'] > best_metrics['sensitivity']:
better = True
elif avg_metrics['sensitivity'] == best_metrics['sensitivity']:
if avg_metrics['specificity'] > best_metrics['specificity']:
better = True
if better:
best_combo = param_set
best_combo_index = combo_index
best_metrics = avg_metrics
Evaluating param combo 1/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=09ee965f4973379bda050e0af8b07bb7). Removing old run: 5c91061ad7104ca99f1bc8b6f054f7cd
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Evaluating param combo 2/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=866bf6c611c544b1a16becf0cb7674ab). Removing old run: 13ebc3c3acbd4116953275ed5f7feff7
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Evaluating param combo 3/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=500d0797d66eb11cdee398dcac2ee0e9). Removing old run: c8f2411d40574f50a7e01a64aaf0304c
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Evaluating param combo 4/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=636eb05e979c6196fb2441a30772aa77). Removing old run: 7c4ce218f8b04b678f4e87e2a50f5bcb
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Evaluating param combo 5/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=9eef1bd532edbf812482fcf4794a0b6d). Removing old run: 9aaf7fd28cf7412983abb7ccdf01301a
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Evaluating param combo 6/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=62dee38dd8a5646163858f2fe7ae5a00). Removing old run: 70c93d67e697404aaa4254c03b71cd81
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Evaluating param combo 7/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=fd87acd1ab479998ff679253ed4cbee0). Removing old run: c9ff2941d1584007a9659ef669a9e90e
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Evaluating param combo 8/16: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=9b979e7549e9eb8a3b749597ec04d2e7). Removing old run: 73684a323c1644a2a4c0af085ab27751
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Evaluating param combo 9/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=001b0a8dcc441eff9e6b8fad1fb158e6). Removing old run: 8ea2d1868b4543699a86b5cdcec20d49
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Evaluating param combo 10/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=b668790ad9b40503a17675e2a080e82b). Removing old run: a9142ea724c947ef9be6ff50228bfe53
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Evaluating param combo 11/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=3f34ed272e80fbe03fd44105e587bcef). Removing old run: 0b514457817649ef9cfd02863c91dfb8
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Evaluating param combo 12/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=6de1910ecc338ba01cb5204f802a7109). Removing old run: 38e7a671514c4259a91f0e7a53aeff07
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Evaluating param combo 13/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=cfb36c53299c39b3af4068021ff76474). Removing old run: 93ae9a3d9c12455baf085a3c6a98393f
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Evaluating param combo 14/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=a3460f9ae7f64a9daef2957b0cabf029). Removing old run: 0e15942674cf41a0bcfae1b4ce4bcaac
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Evaluating param combo 15/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 8}
Duplicate found for this combo (hash=46ceb846705e28850644f75b1e681bf4). Removing old run: d0c8df19d3ca4851a069599c3d215955
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Evaluating param combo 16/16: {'boosted_cb_model__iterations': 100, 'boosted_cb_model__learning_rate': 0.1, 'boosted_cb_model__max_depth': 6, 'boosted_cb_model__num_leaves': 16}
Duplicate found for this combo (hash=9d5b729e51135eafed880be835f768be). Removing old run: eb53c5bb1bc14435bdf2287a04184ad4
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In [221]:
##################################
# Defining a function to
# generate a unique hash for the selected optimal model
##################################
final_model_hash = generate_run_hash(best_combo, mccv_seed=987654321)
existing_final_run_id = find_existing_run_by_hash("boosted_cb_experiment", final_model_hash)
if existing_final_run_id:
print(f"Duplicate final model found (hash={final_model_hash}). Removing previous run: {existing_final_run_id}")
delete_run(existing_final_run_id)
else:
print(f"No duplicate final model found. Proceeding to log new final model.")
Duplicate final model found (hash=09ee965f4973379bda050e0af8b07bb7). Removing previous run: e8a3664cb4c84b618e6067b1efd13fd0
In [222]:
##################################
# Retraining the most optimal CatBoost model
# on the full training data
##################################
print("Optimal Hyperparameter Combination for CatBoost:", best_combo)
final_model = clone(boosted_cb_pipeline)
final_model.set_params(**best_combo)
final_model.fit(X_preprocessed_train, y_preprocessed_train_encoded)
Optimal Hyperparameter Combination for CatBoost: {'boosted_cb_model__iterations': 50, 'boosted_cb_model__learning_rate': 0.01, 'boosted_cb_model__max_depth': 3, 'boosted_cb_model__num_leaves': 8}
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Out[222]:
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_cb_model',
<catboost.core.CatBoostClassifier object at 0x0000013B106CCAA0>)])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('categorical_preprocessor',
ColumnTransformer(force_int_remainder_cols=False,
remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking',
'Physical_Examination',
'Adenopathy', 'Focality',
'Risk', 'T', 'Stage',
'Response'])])),
('boosted_cb_model',
<catboost.core.CatBoostClassifier object at 0x0000013B106CCAA0>)])ColumnTransformer(force_int_remainder_cols=False, remainder='passthrough',
transformers=[('cat', OrdinalEncoder(),
['Gender', 'Smoking', 'Physical_Examination',
'Adenopathy', 'Focality', 'Risk', 'T',
'Stage', 'Response'])])['Gender', 'Smoking', 'Physical_Examination', 'Adenopathy', 'Focality', 'Risk', 'T', 'Stage', 'Response']
OrdinalEncoder()
['Age']
passthrough
<catboost.core.CatBoostClassifier object at 0x0000013B106CCAA0>
In [223]:
##################################
# Evaluating the most optimal CatBoost model
# on the validation data
##################################
y_validation_pred = final_model.predict(X_preprocessed_validation)
validation_metrics = evaluate_binary_metrics(y_preprocessed_validation_encoded, y_validation_pred)
In [224]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_1 = pd.DataFrame({
'hyperparameter': list(best_combo.keys()),
'mccv.optimal.value': list(best_combo.values())
})
consolidated_table_2 = pd.DataFrame({
'metric': list(best_metrics.keys()),
'mccv': [round(v, 4) for v in best_metrics.values()],
'validation': [round(validation_metrics[k], 4) for k in best_metrics.keys()]
})
print(f"{experiment_name} optimal hyperparameter combination:")
display(consolidated_table_1)
print(f"{experiment_name} optimal hyperparameter combination mccv versus validation performance metrics:")
display(consolidated_table_2)
boosted_cb_experiment optimal hyperparameter combination:
| hyperparameter | mccv.optimal.value | |
|---|---|---|
| 0 | boosted_cb_model__iterations | 50.00 |
| 1 | boosted_cb_model__learning_rate | 0.01 |
| 2 | boosted_cb_model__max_depth | 3.00 |
| 3 | boosted_cb_model__num_leaves | 8.00 |
boosted_cb_experiment optimal hyperparameter combination mccv versus validation performance metrics:
| metric | mccv | validation | |
|---|---|---|---|
| 0 | accuracy | 0.8923 | 0.9130 |
| 1 | sensitivity | 0.8800 | 0.9000 |
| 2 | specificity | 0.8977 | 0.9184 |
| 3 | f1 | 0.8349 | 0.8571 |
In [225]:
##################################
# Decoding the true and predicted labels
# for artifact storage
##################################
y_validation_true_decoded = y_encoder.inverse_transform(y_preprocessed_validation_encoded.reshape(-1, 1)).ravel()
y_validation_pred_decoded = y_encoder.inverse_transform(y_validation_pred.reshape(-1, 1)).ravel()
In [226]:
##################################
# Saving the artifacts from the CatBoost model
# hyperparameter tuning exercise
##################################
mccv_csv_path = os.path.join(artifact_dir, f"{model_name}_MCCV_metrics.csv")
cm_path = os.path.join(artifact_dir, f"{model_name}_validation_confusion_matrix.png")
report_path = os.path.join(artifact_dir, f"{model_name}_validation_classification_report.txt")
df_all_combos = pd.DataFrame(all_combos_metrics)
df_all_combos.to_csv(mccv_csv_path, index=False)
log_confusion_matrix(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=cm_path)
log_classification_report(y_validation_true_decoded, y_validation_pred_decoded, labels=class_labels, output_path=report_path)
pickle_model_path = os.path.join(artifact_dir, f"{model_name}_final_model.pkl")
with open(pickle_model_path, "wb") as f:
pickle.dump(final_model, f)
train_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_train_dataset.csv")
thyroid_cancer_preprocessed_train.to_csv(train_dataset_csv_path, index=False)
validation_dataset_csv_path = os.path.join(artifact_dir, f"{model_name}_validation_dataset.csv")
thyroid_cancer_preprocessed_validation.to_csv(validation_dataset_csv_path, index=False)
In [227]:
##################################
# Logging the most optimal CatBoost model
# developed from the full training data
# and evaluated on the validation data
##################################
# Casting integer columns in preprocessed training data to float64
X_logged_input = X_preprocessed_train.astype({
col: 'float64' for col in X_preprocessed_train.select_dtypes(include='int').columns
})
# Preparing the input signature for model logging
input_example = X_logged_input.sample(n=10, random_state=987654321)
signature = infer_signature(input_example, final_model.predict(input_example))
final_run_name = f"CB_HyperparameterCombo_{best_combo_index + 1}_Optimal"
with mlflow.start_run(run_name=final_run_name, description="Retrained on Full Training Set"):
# Storing the hash in MLflow for lookup
mlflow.set_tag("run_hash", final_model_hash)
mlflow.set_tag("model_type", "final_boosted_cb")
mlflow.set_tag("best_combo_index", best_combo_index)
mlflow.log_params(best_combo)
# Logging the average MCCV metrics computed previously
mlflow.log_metrics({f"avg_mccv_{k}": v for k, v in best_metrics.items()})
# Logging validation set metrics
mlflow.log_metrics({f"validation_{k}": v for k, v in validation_metrics.items()})
# Logging the model with input signature
mlflow.sklearn.log_model(
sk_model=final_model,
name="best_boosted_cb_model",
input_example=input_example,
signature=signature
)
# Logging artifacts
mlflow.log_artifact(mccv_csv_path)
mlflow.log_artifact(cm_path)
mlflow.log_artifact(report_path)
mlflow.log_artifact(pickle_model_path)
mlflow.log_artifact(train_dataset_csv_path)
mlflow.log_artifact(validation_dataset_csv_path)
# Logging datasets with metadata (MLflow dataset tracking)
train_dataset = mlflow.data.from_pandas(
pd.read_csv(train_csv_path),
name="mccv_data"
)
mlflow.log_input(train_dataset, context="training")
print("Final CB Model Trained and Logged with Validation Set Evaluation.")
Final CB Model Trained and Logged with Validation Set Evaluation.
1.7.7 Experiment Organization¶
- The MLflow Tracking User Interface (UI) was launched locally at http://127.0.0.1:5000, enabling systematic comparison of all conducted experiments and identification of the optimal run for each ensemble model family
- The bagged_rf_experiment consisted of 16 total runs with the highest model performance metric achieved for RF_HyperparameterCombo_10_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.8310
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8778
- avg_mccv_specificity (MCCV Specificity) = 0.8948
- avg_mccv_accuracy (MCCV Accuracy) = 0.8896
- The boosted_ab_experiment consisted of 8 total runs with the highest model performance metric achieved for AB_HyperparameterCombo_3_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.8403
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8757
- avg_mccv_specificity (MCCV Specificity) = 0.9069
- avg_mccv_accuracy (MCCV Accuracy) = 0.8974
- The boosted_gb_experiment consisted of 16 total runs with the highest model performance metric achieved for GB_HyperparameterCombo_11_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.7850
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.7705
- avg_mccv_specificity (MCCV Specificity) = 0.9181
- avg_mccv_accuracy (MCCV Accuracy) = 0.8729
- The boosted_xgb_experiment consisted of 16 total runs with the highest model performance metric achieved for XGB_HyperparameterCombo_1_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.8264
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8442
- avg_mccv_specificity (MCCV Specificity) = 0.9125
- avg_mccv_accuracy (MCCV Accuracy) = 0.8916
- The boosted_lgbm_experiment consisted of 16 total runs with the highest model performance metric achieved for LGBM_HyperparameterCombo_3_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.8025
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8000
- avg_mccv_specificity (MCCV Specificity) = 0.9162
- avg_mccv_accuracy (MCCV Accuracy) = 0.8806
- The boosted_cb_experiment consisted of 16 total runs with the highest model performance metric achieved for CB_HyperparameterCombo_1_Optimal
- avg_mccv_f1 (MCCV F1 Score) = 0.8348
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8799
- avg_mccv_specificity (MCCV Specificity) = 0.8976
- avg_mccv_accuracy (MCCV Accuracy) = 0.8922
1.7.8 Run Comparison¶
- For the bagged_rf_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.8310 and achieved by the following hyperparameter combination:
- criterion = entropy (against gini)
- max_depth = 3 (against 6)
- min_samples_leaf = 5 (against 10)
- n_estimators = 200 (against 100)
- For the boosted_ab_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.8403 and achieved by the following hyperparameter combination:
- estimator_max_depth = 1 (against 2)
- learning_rate = 0.10 (against 0.01)
- n_estimators = 50 (against 100)
- For the boosted_ab_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.7850 and achieved by the following hyperparameter combination:
- learning_rate = 0.10 (against 0.01)
- max_depth = 3 (against 6)
- min_samples_leaf = 10 (against 5)
- n_estimators = 50 (against 100)
- For the boosted_ab_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.8264 and achieved by the following hyperparameter combination:
- learning_rate = 0.01 (against 0.10)
- max_depth = 3 (against 6)
- gamma 0.10 (against 0.20)
- n_estimators = 50 (against 100)
- For the boosted_ab_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.8025 and achieved by the following hyperparameter combination:
- learning_rate = 0.01 (against 0.10)
- min_child_samples = 3 (against 6)
- num_leaves 8 (against 16)
- n_estimators = 100 (against 200)
- For the boosted_ab_experiment, the highest avg_mccv_f1 (MCCV F1 Score) was 0.8348 and achieved by the following hyperparameter combination:
- learning_rate = 0.01 (against 0.10)
- max_depth = 3 (against 6)
- num_leaves = 8 (against 16)
- iterations = 50 (against 100)
1.7.9 Artifact Storage¶
- For each ensemble algorithm, hyperparameter tuning was performed over a predefined search space. The most performant model from each ensemble experiment was systematically retrained on the full training dataset and logged to MLflow. Model selection was driven by MCCV performance and confirmed via hold-out validation. Each selected model was logged under the convention best_{model_name}_model:
- bagged_rf_experiment: RF_HyperparameterCombo_10_Optimal = best_bagged_rf_model
- criterion = entropy (against gini)
- max_depth = 3 (against 6)
- min_samples_leaf = 5 (against 10)
- n_estimators = 200 (against 100)
- avg_mccv_f1 (MCCV F1 Score) = 0.8310
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8778
- avg_mccv_specificity (MCCV Specificity) = 0.8948
- avg_mccv_accuracy (MCCV Accuracy) = 0.8896
- validation_f1 (Validation F1 Score) = 0.8571
- validation_sensitivity (Validation Sensitivity) = 0.9000
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.9130
- boosted_ab_experiment: AB_HyperparameterCombo_3_Optimal = best_boosted_ab_model
- estimator_max_depth = 1 (against 2)
- learning_rate = 0.10 (against 0.01)
- n_estimators = 50 (against 100)
- avg_mccv_f1 (MCCV F1 Score) = 0.8403
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8757
- avg_mccv_specificity (MCCV Specificity) = 0.9069
- avg_mccv_accuracy (MCCV Accuracy) = 0.8974
- validation_f1 (Validation F1 Score) = 0.8571
- validation_sensitivity (Validation Sensitivity) = 0.9000
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.9130
- boosted_gb_experiment: GB_HyperparameterCombo_11_Optimal = best_boosted_gb_model
- learning_rate = 0.10 (against 0.01)
- max_depth = 3 (against 6)
- min_samples_leaf = 10 (against 5)
- n_estimators = 50 (against 100)
- avg_mccv_f1 (MCCV F1 Score) = 0.7850
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.7705
- avg_mccv_specificity (MCCV Specificity) = 0.9181
- avg_mccv_accuracy (MCCV Accuracy) = 0.8729
- validation_f1 (Validation F1 Score) = 0.8292
- validation_sensitivity (Validation Sensitivity) = 0.8500
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.8985
- boosted_xgb_experiment: XGB_HyperparameterCombo_1_Optimal = best_boosted_xgb_model
- learning_rate = 0.01 (against 0.10)
- max_depth = 3 (against 6)
- gamma 0.10 (against 0.20)
- n_estimators = 50 (against 100)
- avg_mccv_f1 (MCCV F1 Score) = 0.8264
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8442
- avg_mccv_specificity (MCCV Specificity) = 0.9125
- avg_mccv_accuracy (MCCV Accuracy) = 0.8916
- validation_f1 (Validation F1 Score) = 0.8571
- validation_sensitivity (Validation Sensitivity) = 0.9000
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.9130
- boosted_lgbm_experiment: LGBM_HyperparameterCombo_3_Optimal = best_boosted_lgbm_model
- learning_rate = 0.01 (against 0.10)
- min_child_samples = 3 (against 6)
- num_leaves 8 (against 16)
- n_estimators = 100 (against 200)
- avg_mccv_f1 (MCCV F1 Score) = 0.8025
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8000
- avg_mccv_specificity (MCCV Specificity) = 0.9162
- avg_mccv_accuracy (MCCV Accuracy) = 0.8806
- validation_f1 (Validation F1 Score) = 0.8292
- validation_sensitivity (Validation Sensitivity) = 0.8500
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.8985
- boosted_cb_experiment: CB_HyperparameterCombo_1_Optimal = best_boosted_cb_model
- learning_rate = 0.01 (against 0.10)
- max_depth = 3 (against 6)
- num_leaves = 8 (against 16)
- iterations = 50 (against 100)
- avg_mccv_f1 (MCCV F1 Score) = 0.8348
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8799
- avg_mccv_specificity (MCCV Specificity) = 0.8976
- avg_mccv_accuracy (MCCV Accuracy) = 0.8922
- validation_f1 (Validation F1 Score) = 0.8571
- validation_sensitivity (Validation Sensitivity) = 0.9000
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.9130
- bagged_rf_experiment: RF_HyperparameterCombo_10_Optimal = best_bagged_rf_model
- As part of the ensemble model evaluation and selection process, a structured artifact saving pipeline was implemented for each ensemble model for improved reproducibility and traceability. For each model, the following artifacts were generated and saved to both the The MLflow Tracking UI (launched locally at http://127.0.0.1:5000) and the designated directory:
- MCCV_metrics.csv
- Purpose: Stores cross-validation performance metrics for all hyperparameter combinations explored during tuning.
- Contents: Includes classification metrics (Accuracy, Sensitivity, Specificity, F1 Score) for each parameter set.
- File name pattern: {model_name}_MCCV_metrics.csv
- final_model.pkl
- Purpose: Contains the serialized (pickled) optimal model corresponding to the hyperparameter combination that achieved the highest MCCV F1 score.
- Contents: Enables rapid reloading for inference or deployment without retraining.
- File name pattern: {model_name}_final_model.pkl
- validation_classification_report.txt
- Purpose: Captures a full classification report of the optimal model evaluated on the validation set.
- Contents: Precision, Recall, F1-score, Support (per class), Overall Accuracy, Macro Average, and Weighted Average.
- File name pattern: {model_name}_validation_classification_report.txt
- validation_confusion_matrix.png
- Purpose: Visualizes classification results on the validation set via a confusion matrix, providing insight into true positives, true negatives, false positives, and false negatives.
- Contents: PNG image for easy inspection or inclusion in presentations and reports.
- File name pattern: {model_name}_validation_confusion_matrix.png
- MCCV_metrics.csv
1.7.10 Model Registry¶
- The best model run from each of the six ensemble experiments was identified using the MLflow Tracking API:
- bagged_rf_experiment: RF_HyperparameterCombo_10_Optimal = best_bagged_rf_model
- boosted_ab_experiment: AB_HyperparameterCombo_3_Optimal = best_boosted_ab_model
- boosted_gb_experiment: GB_HyperparameterCombo_11_Optimal = best_boosted_gb_model
- boosted_xgb_experiment: XGB_HyperparameterCombo_1_Optimal = best_boosted_xgb_model
- boosted_lgbm_experiment: LGBM_HyperparameterCombo_3_Optimal = best_boosted_lgbm_model
- boosted_cb_experiment: CB_HyperparameterCombo_1_Optimal = best_boosted_cb_model
- For each candidate, both validation performance and Monte Carlo Cross-Validation (MCCV) metrics were retrieved and compared. The primary metric for selection was validation F1 Score, with secondary sorting on the remaining metrics to resolve ties. This composite scoring mechanism ensures the final model is both accurate and well-balanced in class sensitivity:
- validation_f1 (Validation F1 Score)
- validation_sensitivity (Validation Sensitivity)
- validation_specificity (Validation Specificity)
- validation_accuracy (Validation Accuracy)
- avg_mccv_f1 (MCCV F1 Score)
- avg_mccv_sensitivity (MCCV Sensitivity)
- avg_mccv_specificity (MCCV Specificity)
- avg_mccv_accuracy (MCCV Accuracy)
- The best_boosted_ab_model was determined to have the highest cumulative ranking across the validation and MCCV metrics, and was registered directly from its logged run using mlflow.register_model, with the name selected_final_model. Metadata including the experiment name, experiment ID, run ID, model ID, model path and model URI was captured for downstream use.
- validation_f1 (Validation F1 Score) = 0.8571
- validation_sensitivity (Validation Sensitivity) = 0.9000
- validation_specificity (Validation Specificity) = 0.9183
- validation_accuracy (Validation Accuracy) = 0.9130
- avg_mccv_f1 (MCCV F1 Score) = 0.8403
- avg_mccv_sensitivity (MCCV Sensitivity) = 0.8757
- avg_mccv_specificity (MCCV Specificity) = 0.9069
- avg_mccv_accuracy (MCCV Accuracy) = 0.8974
In [228]:
##################################
# Setting the MLflow Client and selecting the final model
# from the optimal candidate models from the previous experiments
# based on the validation and MCCV performance metrics
##################################
client = MlflowClient()
# Listing all experiments for each model type
experiment_names = [
"bagged_rf_experiment",
"boosted_ab_experiment",
"boosted_gbm_experiment",
"boosted_lgbm_experiment",
"boosted_cb_experiment"
]
best_model_info = None
best_evaluation = {
'validation_f1': -1,
'validation_sensitivity': -1,
'validation_specificity': -1,
'validation_accuracy': -1,
'mccv_f1': -1,
'mccv_sensitivity': -1,
'mccv_specificity': -1,
'mccv_accuracy': -1
}
# Evaluating the optimal candidate models to determine the final model
for exp_name in experiment_names:
exp = client.get_experiment_by_name(exp_name)
if exp is None:
continue
runs = client.search_runs(
experiment_ids=[exp.experiment_id],
filter_string="tags.model_type LIKE 'final_%'",
order_by=[
"metrics.validation_f1 DESC",
"metrics.validation_sensitivity DESC",
"metrics.validation_specificity DESC",
"metrics.validation_accuracy DESC",
"metrics.avg_mccv_f1 DESC",
"metrics.avg_mccv_sensitivity DESC",
"metrics.avg_mccv_specificity DESC",
"metrics.avg_mccv_accuracy DESC"
],
max_results=1
)
if not runs:
continue
run = runs[0]
# Extracting model classification performance metrics
val_f1 = run.data.metrics.get("validation_f1", 0)
val_sens = run.data.metrics.get("validation_sensitivity", 0)
val_spec = run.data.metrics.get("validation_specificity", 0)
val_acc = run.data.metrics.get("validation_accuracy", 0)
mccv_f1 = run.data.metrics.get("avg_mccv_f1", 0)
mccv_sens = run.data.metrics.get("avg_mccv_sensitivity", 0)
mccv_spec = run.data.metrics.get("avg_mccv_specificity", 0)
mccv_acc = run.data.metrics.get("avg_mccv_accuracy", 0)
current_model_score = (val_f1, val_sens, val_spec, val_acc, mccv_f1, mccv_sens, mccv_spec, mccv_acc)
best_model_score = tuple(best_evaluation.values())
if current_model_score > best_model_score:
# Obtaining experiment artifact location and normalizing path
artifact_uri = exp.artifact_location
parsed_uri = urlparse(artifact_uri)
if parsed_uri.scheme != "file":
raise RuntimeError(f"Artifact store for experiment {exp.experiment_id} is not local: {artifact_uri}")
exp_path = parsed_uri.path
if re.match(r"^/[a-zA-Z]:", exp_path): # Windows drive letter fix
exp_path = exp_path[1:]
exp_path = os.path.normpath(exp_path)
# Accessing the run's folder inside the experiment folder
run_folder = os.path.join(exp_path, run.info.run_id)
if not os.path.exists(run_folder):
raise FileNotFoundError(f"Run folder not found: {run_folder}")
# Accessing the 'outputs' folder inside the run folder
outputs_folder = os.path.join(run_folder, "outputs")
if not os.path.exists(outputs_folder):
raise FileNotFoundError(f"No 'outputs' folder found in: {run_folder}")
# Dynamically detecting the model ID (m-<uuid> pattern) inside the outputs folder
entries = os.listdir(outputs_folder)
model_id = None
for entry in entries:
if re.match(r"m-[0-9a-f]{32}", entry):
model_id = entry
break
if not model_id:
raise FileNotFoundError(f"No model ID folder found in outputs: {outputs_folder}")
# Building the directory path to models/<model_id>
models_folder = os.path.join(exp_path, "models", model_id)
if not os.path.exists(models_folder):
raise FileNotFoundError(f"Model folder not found: {models_folder}")
model_path = f"file:{models_folder}"
best_model_info = {
"experiment_name": exp_name,
"experiment_id": exp.experiment_id,
"run_id": run.info.run_id,
"model_id": model_id,
"model_path": model_path,
"model_name": "selected_final_model",
"model_uri": f"runs:/{run.info.run_id}/best_{exp_name.split('_')[0]+"_"+exp_name.split('_')[1]}_model"
}
best_evaluation = {
'validation_f1': val_f1,
'validation_sensitivity': val_sens,
'validation_specificity': val_spec,
'validation_accuracy': val_acc,
'mccv_f1': mccv_f1,
'mccv_sensitivity': mccv_sens,
'mccv_specificity': mccv_spec,
'mccv_accuracy': mccv_acc,
}
print("selected final model information:")
display(best_model_info)
selected final model information:
{'experiment_name': 'boosted_ab_experiment',
'experiment_id': '511308871718837045',
'run_id': '37cd7f0c3d24431eade3c9ced4fb5e75',
'model_id': 'm-8c43f6dce7f34501a0c70ac26033b6b1',
'model_path': 'file:d:\\Github_Codes\\ProjectPortfolio\\Portfolio_Project_62\\codes\\mlruns\\511308871718837045\\models\\m-8c43f6dce7f34501a0c70ac26033b6b1',
'model_name': 'selected_final_model',
'model_uri': 'runs:/37cd7f0c3d24431eade3c9ced4fb5e75/best_boosted_ab_model'}
In [229]:
##################################
# Registering the final model
##################################
logging.getLogger("mlflow.tracking._model_registry.fluent").setLevel(logging.ERROR)
if best_model_info:
model_name = best_model_info["model_name"]
model_uri = best_model_info["model_uri"]
# Deleting the existing registered model (if exists)
try:
client.delete_registered_model(name=model_name)
print(f"Deleted model '{model_name}' to reset to version 1.")
except RestException as e:
print(f"Model did not exist or couldn't be deleted: {e}")
# Registering the new best model
result = mlflow.register_model(
model_uri=model_uri,
name=model_name
)
print(f"Registered best model from {best_model_info['experiment_name']} as '{result.name}', version {result.version}")
Deleted model 'selected_final_model' to reset to version 1.
Successfully registered model 'selected_final_model'.
Registered best model from boosted_ab_experiment as 'selected_final_model', version 1
Created version '1' of model 'selected_final_model'.
1.7.11 Model Inference¶
- The most performant model, previously registered under the name selected_final_model, was loaded dynamically from the MLflow Model Registry.
- To ensure compatibility with the model input schema:
- The test dataset was cast to the appropriate types (float64 for numeric and object for categorical features).
- Column types were aligned to match those used during training and logged as part of the model's input signature.
- Predictions were generated using the MLflow-wrapped model via .predict(...).
- The ground truth labels were encoded using an OrdinalEncoder trained on the training labels to maintain consistent class encoding.
- Performance metrics were computed using a custom binary evaluation utility, capturing the following metrics:
- test_f1 (Test F1 Score)
- test_sensitivity (Test Sensitivity)
- test_specificity (Test Specificity)
- test_accuracy (Test Accuracy)
- The final evaluation involving batch inference on the test set confirms the selected model’s ability to generalize to unseen data.
- test_f1 (Test F1 Score) = 0.8518
- test_sensitivity (Test Sensitivity) = 0.8518
- test_specificity (Test Specificity) = 0.9375
- test_accuracy (Test Accuracy) = 0.9120
In [230]:
##################################
# Loading the latest version of the registered model
##################################
model_name = "selected_final_model"
final_model = mlflow.pyfunc.load_model(model_uri=f"models:/{model_name}/latest")
# Casting the test data to match the original schema
X_preprocessed_test = X_preprocessed_test.astype({
'Age': 'float64',
'Gender': 'object',
'Smoking': 'object',
'Physical_Examination': 'object',
'Adenopathy': 'object',
'Focality': 'object',
'Risk': 'object',
'T': 'object',
'Stage': 'object',
'Response': 'object'
})
# Evaluating the final model on the test data
y_test_pred = final_model.predict(X_preprocessed_test)
# Encoding the response variables for model evaluation
y_encoder = OrdinalEncoder()
y_encoder.fit(y_preprocessed_train.values.reshape(-1, 1))
y_preprocessed_train_encoded = y_encoder.transform(y_preprocessed_train.values.reshape(-1, 1)).ravel()
class_labels = list(y_encoder.categories_[0])
y_preprocessed_test_encoded = y_encoder.transform(y_preprocessed_test.values.reshape(-1, 1)).ravel()
test_metrics = evaluate_binary_metrics(y_preprocessed_test_encoded, y_test_pred)
# Extracting the test metrics
test_f1 = test_metrics.get("f1", 0)
test_sensitivity = test_metrics.get("sensitivity", 0)
test_specificity = test_metrics.get("specificity", 0)
test_accuracy = test_metrics.get("accuracy", 0)
In [231]:
##################################
# Consolidating the optimal hyperparameter combination
# and its corresponding MCCV and validation metrics
##################################
consolidated_table_final = pd.DataFrame({
'metric': ['accuracy','sensitivity','specificity','f1'],
'test': [test_accuracy, test_sensitivity, test_specificity, test_f1],
})
print("final selected model test performance metrics:")
display(consolidated_table_final)
final selected model test performance metrics:
| metric | test | |
|---|---|---|
| 0 | accuracy | 0.912088 |
| 1 | sensitivity | 0.851852 |
| 2 | specificity | 0.937500 |
| 3 | f1 | 0.851852 |
1.7.12 Model Deployment¶
- The selected_final_model and its latest version (version 1) was loaded from the MLflow Model Registry and was promoted for production by assigning the alias prod to reference it symbolically for future model loads.
- The aliased production model was successfully loaded as final verification.
In [232]:
##################################
# Promoting the selected_final_model (version 1) to production
# by assigning an alias for symbolic referencing
##################################
if best_model_info:
model_name = best_model_info["model_name"]
version = result.version # version from the registration step
try:
# Assigning an alias to the newly registered model version
client.set_registered_model_alias(
name=model_name,
alias="prod", # or use "champion", or any name you prefer
version=str(version)
)
print(f"Alias 'prod' now points to model version {version} of '{model_name}'")
except RestException as e:
print(f"Error setting alias for model version {version}: {e}")
Alias 'prod' now points to model version 1 of 'selected_final_model'
In [233]:
##################################
# Loading the selected_final_model (version 1) to Production stage
# using the model URI and the assigned alias
##################################
mlflow.pyfunc.load_model(model_uri=f"models:/{model_name}@prod")
Out[233]:
mlflow.pyfunc.loaded_model: artifact_path: file:///d:/Github_Codes/ProjectPortfolio/Portfolio_Project_62/codes/mlruns/511308871718837045/models/m-8c43f6dce7f34501a0c70ac26033b6b1/artifacts flavor: mlflow.sklearn run_id: 37cd7f0c3d24431eade3c9ced4fb5e75
1.9. Consolidated Findings ¶
- This project explored the use of MLflow as an end-to-end MLOps tool for experiment organization, run comparison, artifact storage, model registry management, model inference, and model deployment. The goal was to establish a reproducible workflow for hyperparameter tuning, optimal model selection, and production deployment of an ensemble classification model.
- The project execution process and significant MLflow capabilities leveraged are summarized as follows:
- Experiment Organization (MLflow Tracking UI)
- The MLflow Tracking UI was launched locally to log, organize, and visualize experiments across multiple ensemble algorithms.
- Each experiment captured multiple runs from hyperparameter tuning, enabling systematic review and selection of top-performing candidates per model family.
- Run Comparison (MLflow Search API)
- The MLflow Tracking API was used to query and compare runs based on multiple performance metrics.
- Sorting by primary and secondary metrics allowed precise identification of the optimal run for each ensemble method.
- Artifact Storage (MLflow Logging)
- A structured artifact-saving pipeline was implemented for reproducibility and traceability.
- MLflow stored cross-validation metrics, serialized model objects, validation classification reports, and confusion matrix visualizations alongside each logged run.
- This ensured all necessary model training and evaluation artifacts were versioned and retrievable.
- Model Registry (MLflow Model Registry API)
- The best-performing model from all experiments was programmatically identified and registered in the MLflow Model Registry.
- Metadata such as experiment ID, run ID, model URI, and artifact paths were stored to facilitate future retrieval and reproducibility.
- Model Inference (MLflow PyFunc Models)
- The registered model was dynamically loaded from the registry using its model URI.
- Test datasets were schema-aligned with the training data, and inference was performed directly via the MLflow PyFunc interface.
- Post-inference evaluation confirmed the model’s generalization capability on unseen data.
- Model Deployment (MLflow Model Aliases)
- The final production model version was assigned an alias for symbolic referencing.
- This allowed future production loads via a stable alias URI simplifying CI/CD pipelines and avoiding hardcoded version numbers.
- Experiment Organization (MLflow Tracking UI)
- The end-to-end MLflow pipeline successfully demonstrated how to manage the full lifecycle of machine learning models — from experiment tracking to production deployment — in a reproducible and automated manner:
- MLflow Tracking UI provided intuitive experiment organization and visualization.
- Search API enabled systematic run comparison and metric-based ranking.
- Artifact Logging ensured reproducibility and auditability.
- Model Registry offered centralized version control for candidate and final models.
- PyFunc interfaces simplified inference and integration into downstream applications.
- Model Aliases delivered a modern, stage-free approach to production deployment.
2. Summary ¶
3. References ¶
- [Book] Machine Learning Engineering with MLflow: Manage the End-to-End Machine Learning Life Cycle with MLflow by Natu Lauchande
- [Book] Practical Deep Learning at Scale with MLflow: Bridge the Gap Between Offline Experimentation and Online Production by Yong Liu and Matei Zaharia
- [Book] Implementing MLOps in the Enterprise by Yaron Haviv and Noah Gift
- [Book] Beginning MLOps with MLflow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure by Sridhar Alla and Suman Kalyan Adari
- [Book] Practical Machine Learning on Databricks: Seamlessly Transition ML Models and MLOps on Databricks by Debu Sinha
- [Book] Ensemble Methods for Machine Learning by Gautam Kunapuli
- [Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
- [Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
- [Book] Ensemble Methods: Foundations and Algorithms by Zhi-Hua Zhou
- [Book] Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models (Treading on Python) by Matt Harrison, Edward Krueger, Alex Rook, Ronald Legere and Bojan Tunguz
- [Python Library API] MLflow by MLflow Team
- [Python Library API] NumPy by NumPy Team
- [Python Library API] pandas by Pandas Team
- [Python Library API] seaborn by Seaborn Team
- [Python Library API] matplotlib.pyplot by MatPlotLib Team
- [Python Library API] itertools by Python Team
- [Python Library API] sklearn.experimental by Scikit-Learn Team
- [Python Library API] sklearn.preprocessing by Scikit-Learn Team
- [Python Library API] scipy by SciPy Team
- [Python Library API] sklearn.tree by Scikit-Learn Team
- [Python Library API] sklearn.ensemble by Scikit-Learn Team
- [Python Library API] sklearn.metrics by Scikit-Learn Team
- [Python Library API] xgboost by XGBoost Team
- [Python Library API] lightgbm by LightGBM Team
- [Python Library API] catboost by CatBoost Team
- [Python Library API] StatsModels by StatsModels Team
- [Python Library API] SciPy by SciPy Team
- [Article] MLflow: A Tool for Managing the Machine Learning Lifecycle by MlFlow Team (MLflow.Org)
- [Article] Experiment Tracking with MLflow in 10 Minutes by Kay Jan Wong (Towards Data Science)
- [Article] Getting Started with MLflow Experiment Tracking by DataMapu (Medium)
- [Article] Experiment Tracking and Model Registry with Mlflow by John Thuo (Medium)
- [Article] Machine Learning Experiment tracking using MLflow by Avikumar Talaviya (Medium)
- [Article] A Comprehensive Guide to MLflow for Data Scientists: Real-World Applications and Examples by Radouane El Mahfoud(Medium)
- [Article] End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving by Antons Tocilins-Ruberts (Towards Data Science)
- [Article] Tracking ML Experiments using MLflow by Hafidz Zulkifli (Medium)
- [Article] A Comprehensive Comparison of ML Experiment Tracking Tools by Eryk Lewinson (Towards Data Science)
- [Article] Introduction to DVC and MLflow for Experiment tracking by Haythem Tellili (Medium)
- [Article] How to Track and Visualize Machine Learning Experiments using MLflow by Gurami Keretchashvili (Medium)
- [Article] Experiment Tracking Template with Keras and Mlflow by Lucas Soares (Towards Data Science)
- [Article] Model and Data Versioning: An Introduction to mlflow and DVC by Anant Kumar (Medium)
- [Article] How to Track and Visualize Machine Learning Experiments using MLflow by Gurami Keretchashvili (Medium)
- [Article] Weights and Biases : Experiment Tracking by Yi Yi Chan Myae Win Shein (Medium)
- [Article] A Guide To ML Experiment Tracking – With Weights & Biases by Yash Prakash (Towards Data Science)
- [Article] Weights & Biases: The ML Experiment Tracking Tool That Won’t Make You Cry by Jose S. Gomez-Olea (Medium)
- [Article] MLOps: Weights & Biases Experiments Tracking by Muhammad Faizan (Medium)
- [Article] Intro to MLOps: Machine Learning Experiment Tracking by Weights and Biases Team (Wandb.AI)
- [Article] Ensemble: Boosting, Bagging, and Stacking Machine Learning by Jason Brownlee (MachineLearningMastery.Com)
- [Article] Stacking Machine Learning: Everything You Need to Know by Ada Parker (MachineLearningPro.Org)
- [Article] Ensemble Learning: Bagging, Boosting and Stacking by Edouard Duchesnay, Tommy Lofstedt and Feki Younes (Duchesnay.GitHub.IO)
- [Article] Stack Machine Learning Models: Get Better Results by Casper Hansen (Developer.IBM.Com)
- [Article] GradientBoosting vs AdaBoost vs XGBoost vs CatBoost vs LightGBM by Geeks for Geeks Team (GeeksForGeeks.Org)
- [Article] A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning by Jason Brownlee (MachineLearningMastery.Com)
- [Article] The Ultimate Guide to AdaBoost Algorithm | What is AdaBoost Algorithm? by Ashish Kumar (MyGreatLearning.Com)
- [Article] A Gentle Introduction to Ensemble Learning Algorithms by Jason Brownlee (MachineLearningMastery.Com)
- [Article] Ensemble Methods: Elegant Techniques to Produce Improved Machine Learning Results by Necati Demir (Toptal.Com)
- [Article] The Essential Guide to Ensemble Learning by Rohit Kundu (V7Labs.Com)
- [Article] Develop an Intuition for How Ensemble Learning Works by by Jason Brownlee (Machine Learning Mastery)
- [Article] Mastering Ensemble Techniques in Machine Learning: Bagging, Boosting, Bayes Optimal Classifier, and Stacking by Rahul Jain (Medium)
- [Article] Ensemble Learning: Bagging, Boosting, Stacking by Ayşe Kübra Kuyucu (Medium)
- [Article] Ensemble: Boosting, Bagging, and Stacking Machine Learning by Aleyna Şenozan (Medium)
- [Article] Boosting, Stacking, and Bagging for Ensemble Models for Time Series Analysis with Python by Kyle Jones (Medium)
- [Article] Different types of Ensemble Techniques — Bagging, Boosting, Stacking, Voting, Blending by Abhishek Jain (Medium)
- [Article] Mastering Ensemble Techniques in Machine Learning: Bagging, Boosting, Bayes Optimal Classifier, and Stacking by Rahul Jain (Medium)
- [Article] Understanding Ensemble Methods: Bagging, Boosting, and Stacking by Divya bhagat (Medium)
- [Video Tutorial] MLflow Tutorial | ML Ops Tutorial by CodeBasics (YouTube)
- [Video Tutorial] Introduction To MLflow | Track Your Machine Learning Experiments | MLOps by DSWithBappy (YouTube)
- [Video Tutorial] Complete Tutorial For MLflow | Experiment Tracking by Vikash Das (YouTube)
- [Video Tutorial] Weights and Biases End-To-End Demo by Weights & Biases (YouTube)
- [Video Tutorial] BAGGING vs. BOOSTING vs STACKING in Ensemble Learning | Machine Learning by Gate Smashers (YouTube)
- [Video Tutorial] What is Ensemble Method in Machine Learning | Bagging | Boosting | Stacking | Voting by Data_SPILL (YouTube)
- [Video Tutorial] Ensemble Methods | Bagging | Boosting | Stacking by World of Signet (YouTube)
- [Video Tutorial] Ensemble (Boosting, Bagging, and Stacking) in Machine Learning: Easy Explanation for Data Scientists by Emma Ding (YouTube)
- [Video Tutorial] Ensemble Learning - Bagging, Boosting, and Stacking explained in 4 minutes! by Melissa Van Bussel (YouTube)
- [Video Tutorial] Introduction to Ensemble Learning | Bagging , Boosting & Stacking Techniques by UncomplicatingTech (YouTube)
- [Video Tutorial] Machine Learning Basics: Ensemble Learning: Bagging, Boosting, Stacking by ISSAI_NU (YouTube)
- [Course] DataCamp Python Data Analyst Certificate by DataCamp Team (DataCamp)
- [Course] DataCamp Python Associate Data Scientist Certificate by DataCamp Team (DataCamp)
- [Course] DataCamp Python Data Scientist Certificate by DataCamp Team (DataCamp)
- [Course] DataCamp Machine Learning Engineer Certificate by DataCamp Team (DataCamp)
- [Course] DataCamp Machine Learning Scientist Certificate by DataCamp Team (DataCamp)
- [Course] IBM Data Analyst Professional Certificate by IBM Team (Coursera)
- [Course] IBM Data Science Professional Certificate by IBM Team (Coursera)
- [Course] IBM Machine Learning Professional Certificate by IBM Team (Coursera)