Feature can be used as a

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
• A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
• In following type of feature selection method we start with empty feature set
• 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?
• Let�s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?