August 23, 2020

Support Vector Machines

A Support Vector Machine is a powerful supervised learning model used for classification. An SVM makes classifications by defining a decision boundary and then seeing what side of the boundary an unclassified point falls on.

For a decision boundary using two features, the boundary is called a separating line. For three features, it is called a separating plane. For more than three features, the decision boundary is called a separating hyperplane.

In general, we want a decision boundary to be as far away from the data points as possible.
- Maximizing the distance between the decision boundary and the points will decrease the chance of false classification.

Support Vectors are the points in the training set closest to the decision boundary.

The distance between a support vector and a decision boundary is called the margin. We want to make the margin as large as possible.