Summary of "Generalized Linear Models Tutorial 2 Video 2"

Overfitting can occur in linear regression and logistic regression models when there is less data and more features.

Regularization is used to shrink parameter theta towards zero to reduce overfitting.
L2 Regularization (Ridge Regularization) penalizes squared parameters to suppress large theta values.
L1 Regularization (Lasso Regularization) penalizes the absolute value of parameters to encourage sparse solutions.
Cross-validation is used to select the optimal hyperparameter beta for Regularization.
L2 penalty does not prefer one feature over another, while L1 penalty achieves feature selection by throwing away some features.
The optimal beta value is selected based on accuracy performance on validation data.
logistic regression models with L1 and L2 penalties are evaluated and compared in terms of feature selection and accuracy performance.

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