{"id":24534440,"url":"https://github.com/jennynzhuang/bootstrap_ml_model_evaluation","last_synced_at":"2026-05-07T12:31:19.090Z","repository":{"id":273459359,"uuid":"919787732","full_name":"jennynzhuang/Bootstrap_ML_Model_Evaluation","owner":"jennynzhuang","description":"Enhancing ML Model Evaluation with Bootstrapping","archived":false,"fork":false,"pushed_at":"2025-01-21T02:44:29.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T03:25:55.139Z","etag":null,"topics":["bootstrapping","computational-statistics","jupyter-notebook","machine-learning","python","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jennynzhuang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-21T02:33:26.000Z","updated_at":"2025-01-21T02:45:36.000Z","dependencies_parsed_at":"2025-01-21T03:36:01.601Z","dependency_job_id":null,"html_url":"https://github.com/jennynzhuang/Bootstrap_ML_Model_Evaluation","commit_stats":null,"previous_names":["jennynzhuang/bootstrap_ml_model_evaluation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jennynzhuang%2FBootstrap_ML_Model_Evaluation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jennynzhuang%2FBootstrap_ML_Model_Evaluation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jennynzhuang%2FBootstrap_ML_Model_Evaluation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jennynzhuang%2FBootstrap_ML_Model_Evaluation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jennynzhuang","download_url":"https://codeload.github.com/jennynzhuang/Bootstrap_ML_Model_Evaluation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243797664,"owners_count":20349444,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bootstrapping","computational-statistics","jupyter-notebook","machine-learning","python","scikit-learn"],"created_at":"2025-01-22T11:17:18.288Z","updated_at":"2026-05-07T12:31:19.038Z","avatar_url":"https://github.com/jennynzhuang.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## README for Bootstrap Sampling Project\n\n## Overview\n\nThis 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.\n\nTraditional 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.\n\n## Objectives\n\n- **Dataset**: Use the Scikit-learn Breast Cancer Diagnostics dataset to demonstrate bootstrap sampling in a practical scenario.\n- **Improve Evaluation Methods**: Apply bootstrap sampling to provide more reliable and robust model evaluation metrics.\n- **Understand Variability**: Address issues of variability in traditional train-test splits by leveraging repeated resampling.\n- **Statistical Foundation**: Explore the theoretical underpinnings of bootstrap sampling and its application in statistics and machine learning.\n\n## Key Features\n\n1. **Statistical Explanation of Bootstrap Sampling**:\n   - Detailed exploration of how bootstrap works as a computational statistics technique.\n   - Explanation of how sampling with replacement enables statistical inference without strong assumptions about the underlying data distribution.\n\n2. **Analysis Components**:\n   - Comprehensive explanation of the resampling procedure.\n   - Estimation of bootstrap statistics, including bias and variance of sample estimates.\n   - Calculation of confidence intervals and distributions for key metrics.\n\n3. **Application to Machine Learning**:\n   - Use of bootstrap sampling in model selection and evaluation.\n   - Application to the **Scikit-learn Breast Cancer Diagnostics dataset**, focusing on classification tasks.\n   - Comparison of bootstrap-based evaluation metrics with traditional methods.\n\n## Contents\n\n- **PDF of Presentation Slides**: Visual overview of the project, including key findings and results.\n- **Jupyter Notebook**: Code implementing bootstrap sampling for machine learning model evaluation.\n- **Project Proposal**:\n  - Introduction to the project.\n  - Motivation for using bootstrap sampling.\n  - Objectives and expected outcomes.\n\n## Tools and Technologies\n\n- **Python**:\n  - Libraries: `numpy`, `pandas`, `matplotlib`, `scipy`, `sklearn` for data manipulation, statistical analysis, and machine learning.\n- **Jupyter Notebook**: For interactive exploration and visualization of results.\n\n## How to Use\n- Open the Jupyter notebook to replicate the analysis.\n- Review the presentation slides for a summary of the results and insights.\n- Study the proposal \u0026 understand the motivation and objectives behind the project.\n\n## Outcomes\n\n- A statistically sound framework for evaluating machine learning models.\n- Insights into the variability of model performance metrics.\n- Enhanced understanding of the bias-variance tradeoff through bootstrap analysis.\n\n## Future Work\n\n- Extend the methodology to other resampling techniques, such as cross-validation or jackknife.\n- Apply bootstrap sampling to ensemble methods for improved prediction stability.\n- Explore its use in high-dimensional datasets and advanced machine learning models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjennynzhuang%2Fbootstrap_ml_model_evaluation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjennynzhuang%2Fbootstrap_ml_model_evaluation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjennynzhuang%2Fbootstrap_ml_model_evaluation/lists"}