[amp_mcq option1=”To increase model complexity” option2=”To add more features to the dataset” option3=”To bring all features to a similar scale for better model performance” option4=”To remove irrelevant features” correct=”option3″]<\/p>\n
The correct answer is: C. To bring all features to a similar scale for better model performance.<\/p>\n
Feature scaling is the process of normalizing features to have a similar scale. This is done to prevent the model from being biased towards features with larger scales. Without feature scaling, the model may learn to overfit to the features with larger scales, and may not generalize well to new data.<\/p>\n
Option A is incorrect because feature scaling does not increase model complexity. In fact, it can sometimes simplify the model by making it less sensitive to the scale of the features.<\/p>\n
Option B is incorrect because feature scaling does not add more features to the dataset. It simply changes the scale of the existing features.<\/p>\n
Option D is incorrect because feature scaling does not remove irrelevant features. It simply changes the scale of all features, regardless of whether they are relevant or not.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”To increase model complexity” option2=”To add more features to the dataset” option3=”To bring all features to a similar scale for better model performance” option4=”To remove irrelevant features” correct=”option3″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-43978","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n