SVM: pros and cons

notes from the article

Pros:

1. It works really well with clear margin of separation
2. It is effective in high dimensional spaces.
3. It is effective in cases where 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 related SVC method of Python scikit-learn library.