{"id":24142018,"url":"https://github.com/khushi130404/regulexa","last_synced_at":"2026-05-17T06:38:49.819Z","repository":{"id":269820686,"uuid":"908512884","full_name":"Khushi130404/Regulexa","owner":"Khushi130404","description":"Regulexa is a Python project that showcases and compares Ridge, Lasso, and Elastic-Net regularization techniques in machine learning. 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Regularization helps to prevent overfitting and improves the generalization of models by adding a penalty term to the loss function. This project includes visualizations and performance comparisons for these techniques, making it a valuable resource for data science enthusiasts and machine learning practitioners.\n\n## Regularization Techniques\n\n\n1. Ridge Regression\n- Ridge regression adds a penalty equal to the square of the magnitude of coefficients. It helps to reduce model complexity and multicollinearity.\n- Penalty: α * ||w||₂² (L2 norm)\n- Shrinks coefficients towards zero but never sets them exactly to zero.\n\n\n2. Lasso Regression\n- Lasso regression adds a penalty equal to the absolute value of the coefficients. It performs both variable selection and regularization.\n- Penalty: α * ||w||₁ (L1 norm)\n- Can shrink some coefficients to exactly zero, effectively performing feature selection.\n\n\n3. Elastic-Net Regression\n- Elastic-Net combines both Ridge and Lasso penalties.\n- Penalty: α * [(1 - λ) ||w||₂² + λ ||w||₁]\n- Suitable for datasets with correlated features and when feature selection is required.\n\n\n## Features\n\n- Synthetic Data: Generates synthetic datasets for demonstration purposes.\n\n- Visualizations: Plots showing how regularization affects coefficients and model performance.\n\n- Comparisons: Side-by-side comparison of Ridge, Lasso, and Elastic-Net.\n\n- Metrics: Evaluation using metrics like Mean Squared Error (MSE) and R².\n\n## Tech Used\n\n- Jupyter Notebook: For interactive coding and visualizations.\n\n- scikit-learn (sklearn): For implementing regularization techniques and machine learning models.\n\n- Matplotlib: For creating visualizations.\n\n- NumPy: For numerical computations.\n\n- Pandas: For data manipulation and analysis.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fregulexa","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkhushi130404%2Fregulexa","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fregulexa/lists"}