Supervised Learning - Classification Week 7 Challenge

Q.1 Pros and cons of SVM

Pros:

• Even if input data are non-linear and non-separable, SVMs generate accurate classification results because of their robustness.
• It is useful to solve any complex problem with a suitable kernel function.
• 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.
• SVMs works great for text classification and when finding the best linear separator.

Cons:

• It takes a long training time when working with large datasets.
• It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping
• It is hard to understand the final model and individual impact.

Q.2 Apply SVM to the “Surface defects in stainless steel plates” dataset and evaluate it.

Note : Coding file for this question is uploaded in .ipynb format