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The correct answer is FALSE<\/strong>.<\/p>\n Support vectors are the data points that are on the decision boundary. The decision boundary is the line or surface that separates the two classes of data. The support vectors are the data points that are closest to the decision boundary on both sides.<\/p>\n Here is a diagram that illustrates the concept of support vectors:<\/p>\n [Diagram of a decision boundary with support vectors]<\/p>\n The blue points are the data points from class 1, and the red points are the data points from class 2. The black line is the decision boundary. The support vectors are the blue and red points that are closest to the decision boundary.<\/p>\n In support vector machines, the goal is to find a hyperplane that separates the two classes of data with as much margin as possible. The margin is the distance between the hyperplane and the nearest data points. The support vectors are the data points that determine the margin.<\/p>\n If you remove any of the support vectors, the margin will change. Therefore, the support vectors are the most important data points for the decision boundary.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” 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-54477","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n