[amp_mcq option1=”The proportion of true positives” option2=”The proportion of true positive predictions among all positive predictions” option3=”The proportion of false positives” option4=”The proportion of true positives” correct=”option2″]<\/p>\n
The correct answer is: B. The proportion of true positive predictions among all positive predictions.<\/p>\n
Precision is a measure of a model’s ability to correctly identify positive cases. It is calculated by dividing the number of true positive predictions by the total number of positive predictions. A high precision indicates that the model is good at identifying positive cases, while a low precision indicates that the model is more likely to incorrectly identify negative cases as positive.<\/p>\n
Option A is incorrect because it refers to the proportion of true positives, not the proportion of true positive predictions. Option C is incorrect because it refers to the proportion of false positives, not the proportion of true positive predictions. Option D is incorrect because it refers to the proportion of true positives, not the proportion of true positive predictions.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”The proportion of true positives” option2=”The proportion of true positive predictions among all positive predictions” option3=”The proportion of false positives” option4=”The proportion of true positives” 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":[738],"tags":[],"class_list":["post-55962","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n