What are advantages and disadvantages of SVM?


  1. SVM works relatively well when there is a clear margin of separation between classes.
  2. SVM is more effective in high dimensional spaces.
  3. SVM is effective in cases where the number of dimensions is greater than the number of samples.
  4. SVM is relatively memory efficient


  1. SVM algorithm is not suitable for large data sets.
  2. SVM does not perform very well when the data set has more noise i.e. target classes are overlapping.
  3. In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.
  4. As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.