COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Stepwise regression
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Sequential feature selection
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Neighborhood component selection
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Regularization
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Detailed explanation-1: -Similarly, in lasso regularization a shrinkage estimator reduces the weights (coefficients) of redundant features to zero during training.
Detailed explanation-2: -L1 regularization / Lasso Since each non-zero coefficient adds to the penalty, it forces weak features to have zero as coefficients. Thus L1 regularization produces sparse solutions, inherently performing feature selection.
Detailed explanation-3: -Feature selection methods are intended to reduce the number of input variables to those that are believed to be most useful to a model in order to predict the target variable. Feature selection is primarily focused on removing non-informative or redundant predictors from the model.
Detailed explanation-4: -LASSO Regularization L1 LASSO Regularization is commonly used as a feature selection criterion. It penalizes irrelevant parameters by shrinking their weights or coefficients to zero.