Supervised Learning - Classification Week 7 Challenge

Ans 1. Pros:

a) SVM works well when there is a large number of features.

b) SVM can handle non-linear relationships between features and tagets.

c) SVM tries to find a hyperplane that best seperates the clases by maximizing the margin between them.

d) SVM can handle overfitting, especially when the number of dimensions is greater than number of samples.

Cons:

a) Svm tends to be slower in training when dealing with large datasets.