APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which of these are associated with SVM?
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linear
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non-linear
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hyperplanes
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neighbour
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branch
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Explanation:
Detailed explanation-1: -The optimal approach would be to make margins on the sides and draw an equidistant line from both the margins. This is exactly how SVM tries to classify points by finding an optimal centre line (technically called as hyperplane).
Detailed explanation-2: -Components of SVM. SVM can be linear or non-linear. Linear SVM can be further categorized into hard margin and soft margin. In hard margin SVM, the data is completely linearly separable by a hyperplane, but not in soft margin SVM.
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