Supervised Learning - Classification Week 7 Challenge
- Pros and cons of SVM
Advantages:
-SVM works relatively well when there is a clear margin of separation between classes.
-SVM is more effective in high dimensional spaces.
-SVM is effective in cases where the number of dimensions is greater than the number of samples.
-SVM is relatively memory efficient
Disadvantages:
-SVM algorithm is not suitable for large data sets.
-SVM does not perform very well when the data set has more noise i.e. target classes are overlapping.
-In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.
-As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification.