Statistical Methods for Data Science
Ridge regression is a type of linear regression that includes a regularization term to prevent overfitting by adding a penalty on the size of the coefficients. It is particularly useful when dealing with multiple linear regression models that have multicollinearity, which occurs when predictor variables are highly correlated. By applying ridge regression, one can achieve better model performance and interpretation by shrinking the coefficients toward zero, effectively balancing bias and variance.
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