learn-co-students/dsc-svm-recap-dc-ds-111819

Support Vector Machines - Recap

Introduction

As you saw, support vector machines are another classification tool to add to your repertoire. While computationally expensive, they can be powerful tools providing substantial performance gains in many instances.

Kernel Functions

Probably the most important information worth reviewing is some of the various kernel functions that you can apply:

1. Radial Basis Function (RBF)
   1. c
   2. $\gamma$, which can be specified using gamma in scikit-learn
2. Polynomial Kernel
   1. $\gamma$, which can be specified using gamma in scikit-learn
   2. $r$, which can be specified using coef0 in scikit-learn
   3. $d$, which can be specified using degree in scikit-learn
3. Sigmoid Kernel
   1. $\gamma$, which can be specified using gamma in scikit-learn
   2. $r$, which can be specified using coef0 in scikit-learn

Also recall that in general, c is the parameter for balancing standard accuracy metrics for tuning classifiers with the decision boundary distance.

Summary

While it may appear that this section was a bit brief, Support Vector Machines are a powerful algorithm that deserve attention, so make sure you investigate them properly. Moreover, learning to properly tune SVMs using kernels and an appropriate c value is critical.