Question

For Ridge Regression,if the regularization parameter =0,what does it mean?

(A) Large coefficients are not penalized
(B) Overfitting problems are not accounted for
(C) The loss function is as same as the ordinary least square loss function
(D) All of the above​

Answer 1

Answer:

Regularization. Ridge regression belongs a class of regression tools that use L2 regularization. ... Unlike L1 regularization, L2 will not result in sparse models. A tuning parameter (λ) controls the strength of the penalty term. When λ = 0, ridge regression equals least squares regression.

Explanation:

Answer 2

Answer:

Correct option:

(B) Overfitting problems are not accounted for

Explanation:

For Ridge Regression,if the regularization parameter =0,Overfitting problems are not accounted for Overfitting is a statistical term that describes a modelling error that occurs when a function corresponds too closely to a specific set of data. As a result, overfitting may fail to fit new data, lowering the accuracy of predicting future observations.

Regularization is a technique that aids in the resolution of the over-fitting problem in machine learning models. It is known as regularisation because it assists in keeping the parameters regular or normal. L1 and L2 Regularization, also known as Lasso and Ridge Regression, are common techniques.