[amp_mcq option1=”To make data more complex” option2=”To extract meaningful features” option3=”To perform data cleaning” option4=”To visualize data” correct=”option2″]<\/p>\n
The correct answer is: B. To extract meaningful features<\/strong>.<\/p>\n Feature engineering is the process of using domain knowledge to extract features from raw data that make it more suitable for machine learning. The goal of feature engineering is to create features that are more relevant to the target variable and that can be used to train a more accurate model.<\/p>\n Option A is incorrect because the purpose of feature engineering is to make data more relevant<\/strong>, not more complex.<\/p>\n Option C is incorrect because data cleaning is the process of removing errors and inconsistencies from data.<\/p>\n Option D is incorrect because data visualization is the process of representing data in a graphical form.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”To make data more complex” option2=”To extract meaningful features” option3=”To perform data cleaning” option4=”To visualize data” 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":[735],"tags":[],"class_list":["post-49711","post","type-post","status-publish","format-standard","hentry","category-data-collection-and-preprocessing","no-featured-image-padding"],"yoast_head":"\n