Introduction to Ridge Regression

 

Ridge regression is a regularization technique used to address the issue of multicollinearity in models with multiple independent features. Multicollinearity occurs when there are strong correlations between the independent features, which can lead to instability and overfitting of the model.

In ridge regression, a regularization parameter, typically denoted as alpha, is introduced to the model. This parameter controls the amount of regularization applied to the feature coefficients during model training. By penalizing large coefficients, ridge regression discourages the model from relying too heavily on any single feature or combination of features, thereby reducing overfitting.

The main objective of ridge regression is to find a balance between minimizing the error on the training data and keeping the magnitude of the feature coefficients small. This helps in improving the generalization performance of the model, allowing it to better handle new, unseen data.

In summary, ridge regression is a valuable technique for mitigating multicollinearity and overfitting in regression models with multiple independent features. By introducing a regularization parameter, it helps in stabilizing the model and improving its ability to generalize to new data.