Answer :
Elastic Net is a regularization technique used in linear regression models to prevent overfitting and to handle multicollinearity among the predictor variables. Traditional linear regression can produce models that fit the training data very well, but may not perform adequately on unseen data. Regularization helps by introducing a penalty on the size of the coefficients.
Elastic Net combines two types of regularization:
L1 Regularization (Lasso): This type of regularization adds a penalty equivalent to the absolute value of the magnitude of coefficients. It can shrink some coefficients to zero, thereby performing variable selection and generating sparse models.
L2 Regularization (Ridge): This type of regularization adds a penalty equivalent to the square of the magnitude of coefficients. It helps distribute the coefficient sizes more evenly and works well when predictors are highly multicollinear.
The integration of these techniques in Elastic Net is beneficial in situations where predictors are highly correlated or when the number of predictors exceeds the number of observations.
Therefore, the type of regularization inherent in the Elastic Net case is both L1 and L2 regularization.
Thus, the correct answer is: (D) L1 + L2.
