Supervised Learning - Classification Week 7 Challenge

1) Pros and cons of SVM

• 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 support vector's decision function.
  • SVM is relatively 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
  • As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.
  • In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.

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

The file is attached below.