COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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20
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40
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80
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60
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Detailed explanation-1: -For a 4 class problem, you would have to train the SVM at least 4 times if you are using a one-vs-all method.
Detailed explanation-2: -Multiclass Classification Using SVM The idea is to map data points to high dimensional space to gain mutual linear separation between every two classes. This is called a One-to-One approach, which breaks down the multiclass problem into multiple binary classification problems.
Detailed explanation-3: -If we trained an SVM classifier using a Radial Basis Function (RBF) kernel, then it underfits the training set, so there might be too much regularization. To decrease it, you need to increase the gamma or C hyper-parameter.
Detailed explanation-4: -The effectiveness of SVM depends on the selection of kernel, kernel’s parameters and soft margin parameter C.
Detailed explanation-5: -It tries to find an optimal boundary (known as hyperplane) between different classes. In simple words, SVM does complex data transformations depending on the selected kernel function, and based on those transformations, it aims to maximize the separation boundaries between your data points.