MCQ IN COMPUTER SCIENCE & ENGINEERING

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: -The best hyperplane is that plane that has the maximum distance from both the classes, and this is the main aim of SVM. This is done by finding different hyperplanes which classify the labels in the best way then it will choose the one which is farthest from the data points or the one which has a maximum margin.

Detailed explanation-3: -SVM or Support Vector Machine is a linear model for classification and regression problems. It can solve linear and non-linear problems and work well for many practical problems. The idea of SVM is simple: The algorithm creates a line or a hyperplane which separates the data into classes.

Detailed explanation-4: -c. They find “maximum margin” classifiers. It’s a true statement that Support Vector Machines (SVMs) find “maximum margin” classifiers. SVMs are used and helpful when one wants the classifier to have the largest margin possible because they maximize the margin.

Detailed explanation-5: -Support vectors are the data points that are close to the decision boundary, they are the data points most difficult to classify, they hold the key for SVM to be optimal decision surface. The optimal hyperplane comes from the function class with the lowest capacity i.e minimum number of independent features/parameters.