Supervised Learning - Classification Week 7 Challenge

Question 1: Pros and cons of SVM?

Pros of SVM:

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 called as memory efficient.

Cons of SVM:

1. It does not perform well when we have large data set because the required training time is higher.

2. It does not perform very well, when the dataset has more noise.,ie target classes are overlapping.

3. SBM doesn't directly provide probability estimates, they are calculated using expensive five fold cross validation. it is included in the related SVC method of python scikit learn library.

Question 2: Apply SVM to the “Surface defects in stainless steel plates” dataset and evaluate it?

Answer: please have the attached pdf for the code & output in python.