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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The SVM allows very low error in classification
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The SVM allows high amount of error in classification
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None of the above
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None of the above
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Detailed explanation-1: -A) The SVM allows a very low error in classification. B) The SVM allows a high amount of error in the classification. C) None of the above. Solution: A. Explanation: A hard margin means that an SVM is very rigid in classification and tries to work extremely well in the training set, causing overfitting.
Detailed explanation-2: -Maximizing the margin seems good because points near the decision surface represent very uncertain classification decisions: there is almost a 50% chance of the classifier deciding either way. A classifier with a large margin makes no low certainty classification decisions.
Detailed explanation-3: -The difference between a hard margin and a soft margin in SVMs lies in the separability of the data. If our data is linearly separable, we go for a hard margin. However, if this is not the case, it won’t be feasible to do that.
Detailed explanation-4: -If we keep all instances off the street and on the right side, this is called hard margin classification. There are two main issues with hard margin classification. Hard Margin Classification only works if the data is linearly separable also Hard Margins are very sensitive to outliers.