APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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How far the hyperplane is from the support vectors
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How accurately the SVM can predict outcomes for unseen data
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The threshold amount of error in an SVM
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None of the above
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Detailed explanation-1: -What do you mean by generalization error in terms of the SVM? Explanation: Generalisation error in statistics is generally the out-of-sample error, which measures how accurately a model can predict values for previously unseen data.
Detailed explanation-2: -Accuracy can be computed by comparing actual test set values and predicted values. Well, you got a classification rate of 96.49%, considered as very good accuracy. For further evaluation, you can also check precision and recall of model.
Detailed explanation-3: -SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point.
Detailed explanation-4: -The effectiveness of SVM depends on the selection of kernel, kernel’s parameters and soft margin parameter C. . Each pair of parameters is checked using cross validation, and the parameters with best cross validation accuracy are picked.