COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
data points are classified partially
|
|
|
all data points be classified correctly
|
|
|
data points are misclassified
|
|
|
none of above
|
Detailed explanation-1: -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.
Detailed explanation-2: -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-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: -In hard margin SVM there are, by definition, no misclassifications. This indeed means that hard margin SVM tries to minimize ‖w‖2. Due to the formulation of the SVM problem, the margin is 2/‖w‖. As such, minimizing the norm of w is geometrically equivalent to maximizing the margin.