Ridge Regression

 

Ridge regression is a technique used to prevent overfitting in polynomial regression models, especially when dealing with multiple independent variables or features. Overfitting occurs when the model captures noise in the training data and fails to generalize well to unseen data.

In ridge regression, a regularization parameter called Alpha is introduced. Alpha controls the magnitude of the polynomial coefficients during model training. As the value of Alpha increases, the polynomial coefficients decrease in magnitude, which helps prevent overfitting by reducing the model's reliance on any single feature or combination of features.

Choosing the right value of Alpha is crucial in ridge regression. If Alpha is too large, the coefficients may approach zero, leading to underfitting of the data. On the other hand, if Alpha is too small or zero, overfitting may occur. Therefore, cross-validation is often used to select the optimal value of Alpha that maximizes the model's performance on validation data.

To make predictions using ridge regression, one can use the ridge object from the scikit-learn linear models module. After training the model with the fit method, predictions can be made using the predict method.

In summary, ridge regression is a valuable technique for preventing overfitting in polynomial regression models with multiple features. By introducing a regularization parameter, it helps control the complexity of the model and improves its generalization performance on unseen data.