Supervised Learning - Classification Week 7 Challenge

ADVT:-

• SVM works relatively well when there is a clear margin of separation between classes.
• more effective in high dimensional spaces becaus kernal tric
• SVM is effective in cases where the number of dimensions is greater than the number of samples this is used in computer vision for reduced the dimentanality.
• SVM is relatively memory efficient

Disadvantage:-

• One must do feature scaling of variables before applying SVM.
• SVM takes a long training time on large datasets.
• SVM model is difficult to understand and interpret by human beings unlike Decision Trees.