Machine Learning Model Training and Evaluation Framework

This project is a modular framework for training machine learning models, managing checkpoints, and evaluating results. It simplifies hyperparameter tuning, performance evaluation, and visualization for multiple classification models.

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Table of Contents

1. Overview
2. Features
3. Project Structure
4. Setup Instructions
5. Usage
6. Visualizations
7. Dependencies
8. Future Improvements

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Overview

This project automates key steps in machine learning workflows:

• Train models with hyperparameter tuning using GridSearchCV.
• Save model results, predictions, and confusion matrices as checkpoints.
• Resume training from checkpoints when interrupted.
• Visualize model performance using ROC Curves, Precision-Recall Curves, and Confusion Matrices.

The project is built for binary classification problems, particularly those requiring probabilistic outputs for model evaluation.

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Features

1. Checkpoint Management
   Save and load checkpoints, including trained models, evaluation metrics, and predictions.

2. Model Training

   • Perform hyperparameter tuning with GridSearchCV.
   • Compute evaluation metrics such as accuracy, F1 score, precision, recall, and ROC AUC.
   • Save results and model probabilities for further analysis.

3. Result Visualization

   • ROC Curve: Assess True Positive and False Positive tradeoffs.
   • Precision-Recall Curve: Evaluate precision vs. recall.
   • Confusion Matrices: Visualize misclassifications.

4. Cross-Validation
   Automatically calculates cross-validation scores and averages.

5. Modularity
   Designed to easily add new models or extend functionality.

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Setup Instructions

1. Clone the Repository

   git clone https://github.com/your-username/ml-training-framework.git
   cd ml-training-framework
   
   

Visualizations

• ROC Curve

• Precision-Recall Curve

• Confusion Matrix

Dependencies

• Python 3.8+
• Scikit-learn
• Pandas
• Matplotlib
• NumPy

Install all dependencies using:

pip install -r requirements.txt

Future Improvements

• Add support for multiclass classification.
• Integrate additional hyperparameter tuning techniques (e.g., RandomizedSearchCV).
• Enhance visualizations with interactive dashboards (e.g., Plotly).