Regularization and Feature Engineering: Ridge, Lasso and Leak-Free Pipelines

Why do models with more features often get worse on new data, and how do you fix it? In this episode we build the full toolkit: polynomial features, categorical encoding, scaling, multicollinearity, ridge regression with a full derivation of its closed form, lasso, elastic net, and the geometry that explains why L1 creates sparsity. We work through small numerical examples by hand, then finish with feature selection and leakage-free preprocessing using scikit-learn pipelines, so your validation scores can actually be trusted. This is episode 17 of the series Machine Learning from Zero to Advanced. Next up: k-nearest neighbors and Naive Bayes. 0:00 Intro 0:57 Polynomial Features 2:44 Categorical Encoding 4:10 Scaling 5:49 Multicollinearity 7:29 Ridge Regression 10:48 L1 vs L2 Geometry 12:58 Lasso and Elastic Net 14:55 Feature Selection 15:41 Pipelines and Leakage 17:42 Recap #MachineLearning #Regularization #FeatureEngineering #RidgeRegression #Lasso