Supervised Learning - Classification Week 7 Challenge

1)

Pros:

1)It works really well with a clear margin of separation.

2)It is effective in high dimensional spaces.

3)It is effective in cases where the number of dimensions is greater than the number of samples.

4)It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.

Cons:

1)It doesn’t perform well when we have large data set because the required training time is higher

2)It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping

3)SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is included in the related SVC method of Python scikit-learn library.