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.
Category
Educational
