Ridge Regression

Ridge Regression, also known as L2 regularization, is a technique used in regression models, primarily to prevent overfitting. It adds a penalty equivalent to the square of the magnitude of coefficients to the loss function.

Balancing Bias and Variance with Ridge Regression

Ridge Regression shrinks the coefficients and reduces model complexity, thus balancing the trade-off between bias and variance. It's particularly useful when dealing with multicollinearity or when the number of predictors exceeds the number of observations.

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