https://github.com/jennynzhuang/bootstrap_ml_model_evaluation

Enhancing ML Model Evaluation with Bootstrapping
https://github.com/jennynzhuang/bootstrap_ml_model_evaluation

bootstrapping computational-statistics jupyter-notebook machine-learning python scikit-learn

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Enhancing ML Model Evaluation with Bootstrapping

• Host: GitHub
• URL: https://github.com/jennynzhuang/bootstrap_ml_model_evaluation
• Owner: jennynzhuang
• Created: 2025-01-21T02:33:26.000Z (3 days ago)
• Default Branch: main
• Last Pushed: 2025-01-21T02:44:29.000Z (3 days ago)
• Last Synced: 2025-01-21T03:25:55.139Z (3 days ago)
• Topics: bootstrapping, computational-statistics, jupyter-notebook, machine-learning, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## README for Bootstrap Sampling Project

## Overview

This project focuses on applying **bootstrap sampling**, a powerful resampling method, to improve the evaluation of machine learning models. The project uses the **Scikit-learn Breast Cancer Diagnostics Dataset**, leveraging bootstrap techniques to provide robust performance metrics for predictive models.

Traditional evaluation methods, such as single train-test splits, often suffer from variability due to the random nature of data partitioning. Bootstrap sampling offers a statistically grounded solution, allowing for more comprehensive model performance analysis through repeated resampling of the dataset.

## Objectives

- **Dataset**: Use the Scikit-learn Breast Cancer Diagnostics dataset to demonstrate bootstrap sampling in a practical scenario.

- **Improve Evaluation Methods**: Apply bootstrap sampling to provide more reliable and robust model evaluation metrics.

- **Understand Variability**: Address issues of variability in traditional train-test splits by leveraging repeated resampling.

- **Statistical Foundation**: Explore the theoretical underpinnings of bootstrap sampling and its application in statistics and machine learning.

## Key Features

1. **Statistical Explanation of Bootstrap Sampling**:

   - Detailed exploration of how bootstrap works as a computational statistics technique.

   - Explanation of how sampling with replacement enables statistical inference without strong assumptions about the underlying data distribution.

2. **Analysis Components**:

   - Comprehensive explanation of the resampling procedure.

   - Estimation of bootstrap statistics, including bias and variance of sample estimates.

   - Calculation of confidence intervals and distributions for key metrics.

3. **Application to Machine Learning**:

   - Use of bootstrap sampling in model selection and evaluation.

   - Application to the **Scikit-learn Breast Cancer Diagnostics dataset**, focusing on classification tasks.

   - Comparison of bootstrap-based evaluation metrics with traditional methods.

## Contents

- **PDF of Presentation Slides**: Visual overview of the project, including key findings and results.

- **Jupyter Notebook**: Code implementing bootstrap sampling for machine learning model evaluation.

- **Project Proposal**:

  - Introduction to the project.

  - Motivation for using bootstrap sampling.

  - Objectives and expected outcomes.

## Tools and Technologies

- **Python**:

  - Libraries: `numpy`, `pandas`, `matplotlib`, `scipy`, `sklearn` for data manipulation, statistical analysis, and machine learning.

- **Jupyter Notebook**: For interactive exploration and visualization of results.

## How to Use

- Open the Jupyter notebook to replicate the analysis.

- Review the presentation slides for a summary of the results and insights.

- Study the proposal & understand the motivation and objectives behind the project.

## Outcomes

- A statistically sound framework for evaluating machine learning models.

- Insights into the variability of model performance metrics.

- Enhanced understanding of the bias-variance tradeoff through bootstrap analysis.

## Future Work

- Extend the methodology to other resampling techniques, such as cross-validation or jackknife.

- Apply bootstrap sampling to ensemble methods for improved prediction stability.

- Explore its use in high-dimensional datasets and advanced machine learning models.