What are common feature selection methods in regression task?

Related MCQs

• We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables 3. Feature normalization always helps when we use Gaussian kernel in SVM
• We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1.We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables 3. Feature normalization always helps when we use Gaussian kernel in SVM
• We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
• We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
• We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we use Gaussian kernel in SVM
• Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
• Suppose we fit �Lasso Regression� to a data set, which has 100 features (X1,X2�X100).� Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),� and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit �Lasso Regression� to a data set, which has 100 features (X1,X2�X100).� Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),� and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
• Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?