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 this is 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: -A hyperplane is a decision boundary that differentiates the two classes in SVM. A data point falling on either side of the hyperplane can be attributed to different classes. The dimension of the hyperplane depends on the number of input features in the dataset.
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.
Detailed explanation-3: -Types of SVM such as maximum-margin classifier, soft-margin classifier, support vector machine.
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