Supervised Learning - Classification Week 7 Challenge

Ans 1

• Pros:
  • It works really well with a clear margin of separation
  • It is effective in high dimensional spaces.
  • It is effective in cases where the number of dimensions is greater than the number of samples.
  • It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
• 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. It is included in the related SVC method of Python scikit-learn library.

Ans 2 in Attachment