Regularization (L1 / L2)

Summary

Adding a penalty for large model weights to reduce overfitting — L1 (Lasso) can shrink some weights to exactly zero (feature selection), L2 (Ridge) shrinks all weights smoothly toward zero.

How to Learn This

1Fit a regularized regression model and compare it to an unregularized version.
2Learn why L1 regularization can effectively remove unimportant features.
3Experiment with different regularization strengths and observe the effect on overfitting.

Hyperparameter Tuning (Grid / Random Search)

Ensemble Methods

More topics in Model Evaluation & Tuning

Classification Metrics (Accuracy, Precision, Recall, F1)Confusion Matrix ROC-AUC Curve Regression Metrics (MAE, RMSE, R²)Hyperparameter Tuning (Grid / Random Search)Ensemble Methods Model Selection Strategy

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