Supervised Learning - Classification Week 7 Challenge

1.Pros:

1. It is really effective in the higher dimension.
2. Effective when the number of features are more than training examples.
3. Best algorithm when classes are separable
4. The hyperplane is affected by only the support vectors thus outliers have less impact.
5. SVM is suited for extreme case binary classification.

cons:

1. For larger dataset, it requires a large amount of time to process.
2. Does not perform well in case of overlapped classes.
3. Selecting, appropriately hyperparameters of the SVM that will allow for sufficient generalization performance.
4. Selecting the appropriate kernel function can be tricky.