Supervised Learning - Classification Week 7 Challenge

Answer 1:

Pros:

• It works really well with clear margin of separation and high dimesional space.
• It offers good accuracy and faster prediction.
• Effective in cases where number of dimensions is greater than the number of samples
• It is memory efficient as it uses a subset of training points in the decision function (called support vectors).

Cons:

• It doesn’t perform well when we have large data set because the required training time is higher.
• It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping.
• SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.