SVM

What is it

• method for supervised classification, regression or outlier detection
• devides the classes by a gab as wide as possible (https://en.wikipedia.org/wiki/Support_vector_machine)
• linear and non-linear with kernel trick

pros:

• effective in high dimensional space
• kernels can be specified for decision functions

cons:

• SVMs do not directly provice probability (maybe use Platt scaling for this?)

Scikit has overview of support vector machine functions: http://scikit-learn.org/stable/modules/svm.html