17.3 Advantages and Disadvantages of SVM

Performs well at classifying non-linear data

Optimizing margins can help reduce the overfitting of data and allow for capacity control

Learning without a local minima

There are many kernels (transformations) that could be used to fit the data unlike any other algorithm

Often provides sparse solutions

Performs well on datasets that have many attributes, even if there are relatively very few cases on which to train the model

Choice of the right Kernel

• The number of possible kernels is infinite and can make it hard to choose the right one

• Most software uses a few kernels that generalize to many situations, but no kernel generalizes to every situation

Can be computationally intensive

• Algorithms can be complex

Can overfit the model