[amp_mcq option1=”the svm allows very low error in classification” option2=”the svm allows high amount of error in classification” option3=”both 1 & 2″ option4=”none of the above” correct=”option1″]<\/p>\n
The correct answer is: A. the svm allows very low error in classification.<\/p>\n
A hard margin is a type of support vector machine (SVM) that minimizes the classification error by finding a hyperplane that separates the data points into two classes with as much margin as possible. This means that the hyperplane will be as far away as possible from any data points, which will result in a lower classification error.<\/p>\n
A soft margin is a type of SVM that allows for a small amount of error in classification. This is done by allowing the hyperplane to be closer to some of the data points. This results in a lower classification error than a hard margin, but it also results in a less accurate classification.<\/p>\n
Option B is incorrect because a hard margin does not allow for a high amount of error in classification. Option C is incorrect because a hard margin does not allow for both low and high error in classification. Option D is incorrect because a hard margin is a type of SVM.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”the svm allows very low error in classification” option2=”the svm allows high amount of error in classification” option3=”both 1 & 2″ option4=”none of the above” 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-55300","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n