Support Vector Regression

SVMs are binary classifiers and are usually used in classification problems (you will learn more about this in Chapter 3, An Introduction to Classification). An SVM classifier takes data and tries to predict which class it belongs to. Once the classification of a data point is determined, it gets labeled. But SVMs can also be used for regression; that is, instead of labeling data, it can predict future values in a series.

The SVR model uses the space between our data as a margin of error. Based on the margin of error, it makes predictions regarding future values.

If the margin of error is too small, we risk overfitting the existing dataset. If the margin of error is too big, we risk underfitting the existing dataset.

In the case of a classifier, the kernel describes the surface dividing the state space, whereas, in a regression, the kernel measures the margin of error. This kernel can use a linear model, a polynomial model, or many other possible...