[amp_mcq option1=”0.91″ option2=”0.09″ option3=”0.9″ option4=”0.95″ correct=”option1″]<\/p>\n
The correct answer is A. 0.91.<\/p>\n
Precision is the fraction of predicted positives that are actually positive. In other words, it is the probability that a positive prediction is correct.<\/p>\n
To calculate precision, we divide the number of true positives by the total number of predicted positives. In the confusion matrix, the number of true positives is 40, and the total number of predicted positives is 44. Therefore, the precision is 40 \/ 44 = 0.91.<\/p>\n
Option B is incorrect because it is the recall value, which is the fraction of actual positives that are predicted positive. In other words, it is the probability that a positive instance is correctly identified as positive.<\/p>\n
Option C is incorrect because it is the accuracy value, which is the fraction of all instances that are correctly classified. In other words, it is the probability that an instance is correctly identified as either positive or negative.<\/p>\n
Option D is incorrect because it is the F1 score, which is a measure of a model’s performance that takes into account both precision and recall. It is calculated as the harmonic mean of precision and recall.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”0.91″ option2=”0.09″ option3=”0.9″ option4=”0.95″ correct=”option1″]<\/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-56590","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n