{"id":28924025,"url":"https://github.com/allenleizhao/model_validation_strategy_comparison","last_synced_at":"2026-05-06T10:38:22.050Z","repository":{"id":297534140,"uuid":"997092873","full_name":"AllenLeiZhao/Model_Validation_Strategy_Comparison","owner":"AllenLeiZhao","description":"A comparison of cross-validation techniques and classification models using synthetic data. 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By applying multiple machine learning models and systematically increasing the number of repeats, we analyze how performance stability improves with more validation cycles.\n\n---\n\n## 🎯 Project Highlights\n\n- Generated synthetic data using `make_classification` from `sklearn.datasets`\n- Compared model performance under standard K-Fold and Repeated K-Fold settings\n- Evaluated multiple classifiers: Logistic Regression, Random Forest, and SVM (RBF Kernel)\n- Visualized accuracy trends across repeat counts using boxplots\n\n---\n\n## 📊 Visualizations\n\nKey visual output:\n\n![Sample Visualization](assets/img.png)\n\n---\n\n## ✅ Techniques Used\n\n- Synthetic dataset creation (`sklearn.datasets.make_classification`)\n- Cross-validation strategies: `KFold`, `RepeatedKFold`, and `cross_val_score`\n- Classification Models: Logistic Regression, Random Forest, SVM (RBF)\n- Feature scaling with `StandardScaler`\n- Model evaluation: Accuracy, Standard Error of Mean (SEM)\n- Visualization: `matplotlib.pyplot`\n\n---\n\n## 📁 Files\n\n- `/code/` – Python Notebook (`.ipynb`) containing all experiments\n- `/assets/` – Plots\n- `README.md` – You are here\n\n---\n\n## 📊 Key Findings\n\n- Single 10-fold accuracy for logistic regression: **~86.8%**\n- Repeating folds up to 15 times smooths performance fluctuations significantly\n- Random Forest and SVM with RBF kernel outperform linear models:\n  - Random Forest Accuracy: **~92.1%**\n  - SVM (RBF) Accuracy: **~96.5%**\n- SVM performance suggests non-linear relationships in the data\n\n---\n\n## 🙋‍♂️ About Me\n\nI'm currently pursuing a Master’s in Analytics with hands-on experience in machine learning and data visualization. My projects combine technical depth with practical interpretation using tools like **Python**, **R**, **Tableau**, and **Looker Studio**.\n\n---\n\n## 📬 Contact\n\nFeel free to connect via [LinkedIn](https://www.linkedin.com/in/allen-lei-zhao/) or reach out via email: `allen.lei.zhao@gmail.com`.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallenleizhao%2Fmodel_validation_strategy_comparison","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fallenleizhao%2Fmodel_validation_strategy_comparison","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fallenleizhao%2Fmodel_validation_strategy_comparison/lists"}