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The correct answer is: FALSE<\/strong>.<\/p>\n A linear SVM classifier does not work by drawing a straight line between two classes. Instead, it works by finding a hyperplane that separates the data points into two classes as best as possible. A hyperplane is a flat surface in a higher-dimensional space that separates the data points into two classes.<\/p>\n The hyperplane that is found by a linear SVM classifier is the one that maximizes the margin between the two classes. The margin is the distance between the hyperplane and the closest data points on either side of the hyperplane.<\/p>\n The following figure shows an example of a linear SVM classifier. The data points are shown as blue circles and red squares. The hyperplane is shown as a solid black line. The margin is shown as the dashed black lines.<\/p>\n As you can see, the hyperplane separates the data points into two classes very well. The margin is also very large, which means that the hyperplane is very confident in its decision.<\/p>\n I hope this explanation was helpful. Let me know if you have any other questions.<\/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-45927","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n