COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Ridge regression uses subset selection of features
|
|
|
Lasso regression uses subset selection of features
|
|
|
Both uses subset selection of features
|
|
|
None of them are used for subset selection of features
|
Detailed explanation-1: -Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection? “Ridge regression” will use all predictors in final model whereas “Lasso regression” can be used for feature selection because coefficient values can be zero.
Detailed explanation-2: -The cost function for both ridge and lasso regression are similar. However, ridge regression takes the square of the coefficients and lasso takes the magnitude. Lasso regression can be used for automatic feature selection, as the geometry of its constrained region allows coefficient values to inert to zero.
Detailed explanation-3: -Ridge regression uses L2 on the other hand lasso regression go uses L1 regularisation technique. In ridge regression, the penalty is equal to the sum of the squares of the coefficients and in the Lasso, penalty is considered to be the sum of the absolute values of the coefficients.
Detailed explanation-4: -LASSO, short for Least Absolute Shrinkage and Selection Operator, is a statistical formula whose main purpose is the feature selection and regularization of data models.
Detailed explanation-5: -Lasso regression is used in machine learning to prevent overfitting. It is also used to select features by setting coefficients to zero. Lasso regression is also called L1-norm regularization.