[amp_mcq option1=”the output attribute must be categorical.” option2=”the output attribute must be numeric.” option3=”the resultant model is designed to determine future outcomes.” option4=”the resultant model is designed to classify current behavior.” correct=”option3″]<\/p>\n
The correct answer is C. The resultant model is designed to determine future outcomes.<\/p>\n
A prediction problem is a type of machine learning problem where the goal is to predict an output value for a given input. The output value can be either categorical or numeric. For example, in a spam filtering problem, the output value is a binary value (spam or not spam). In a house price prediction problem, the output value is a numeric value (the price of the house).<\/p>\n
The resultant model is designed to determine future outcomes. This means that the model can be used to predict the output value for new inputs that were not seen during training. For example, a spam filtering model can be used to predict whether a new email is spam or not. A house price prediction model can be used to predict the price of a new house.<\/p>\n
Options A and B are incorrect because the output attribute can be either categorical or numeric. Option D is incorrect because the resultant model is designed to determine future outcomes, not classify current behavior.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”the output attribute must be categorical.” option2=”the output attribute must be numeric.” option3=”the resultant model is designed to determine future outcomes.” option4=”the resultant model is designed to classify current behavior.” 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":[729],"tags":[],"class_list":["post-56998","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n