[amp_mcq option1=”both are true” option2=”1 is true and 2 is false” option3=”both are false” option4=”1 is false and 2 is true” correct=”option2″]<\/p>\n
The correct answer is: B. 1 is true and 2 is false<\/strong><\/p>\n Explanation:<\/strong><\/p>\n This is true because filter methods do not need to train a model for each subset of features, while wrapper methods do. Filter methods simply rank the features based on some criteria, such as correlation with the target variable, and then select the top-ranked features. Wrapper methods, on the other hand, train a model for each subset of features and then select the subset that results in the best model performance. This is a more computationally expensive process.<\/p>\n This is false. Both filter methods and wrapper methods can use statistical methods for evaluating a subset of features. However, filter methods typically use simpler statistical methods, such as correlation, while wrapper methods typically use more complex methods, such as cross-validation.<\/p>\n In conclusion, filter methods are faster than wrapper methods because they do not need to train a model for each subset of features. Both filter methods and wrapper methods can use statistical methods for evaluating a subset of features, but filter methods typically use simpler methods while wrapper methods typically use more complex methods.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”both are true” option2=”1 is true and 2 is false” option3=”both are false” option4=”1 is false and 2 is true” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-46860","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n\n
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