Answer :

Ridge regression employs L2 regularization, which adds a penalty to the loss function based on the square of the coefficients' magnitude, to prevent overfitting and reduce model complexity. Correct option is 2.

In the context of machine learning models, regularization is a technique used to prevent overfitting by discouraging overly complex models. The Ridge regression model, specifically, applies a type of regularization known as L2 regularization. This method adds a penalty to the loss function equivalent to the square of the magnitude of the coefficients.

The key purpose of L2 regularization is to shrink the coefficients towards zero, but not exactly to zero, which helps in reducing model complexity and multicollinearity. Ridge regression is distinct from other types of models that may use different regularization techniques.

For instance, Lasso regression applies L1 regularization, which can actually reduce some coefficients to zero, effectively performing feature selection. There is also Elastic Net, which combines both L1 and L2 regularization methods, thus blending properties of both Ridge and Lasso regression. Regularization techniques such as L2 are essential in building models that generalize well to new, unseen data. Correct option is 2.

Thanks for taking the time to read Which regularization is relevant to a Ridge model 1 Neither 2 L2 3 L1 4 L1 and L2. We hope the insights shared have been valuable and enhanced your understanding of the topic. Don�t hesitate to browse our website for more informative and engaging content!

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