Support Vector Regression

Support Vector Regression is a powerful Model that works on the principles of Support Vector Classification. However, it is used for Regression problems. You'll learn more about it in this lesson.

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Support Vector Regression

The idea of Support Vector Regression has been borrowed from Support Vector Machines. In classification, we predict a discrete-valued output. Here are some things to note:

  • As the name suggests Support Vector Regression is used for predicting the real-valued output.

  • The model produced by Support Vector Regression depends only on a subset of the training data.

  • There is a concept of Kernel, which involves mapping the features or columns or dimensions to higher dimensions to make the problem solvable.

Implementation in Scikit Learn

SVR class implements the Support Vector Regression.

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