SVM Regression Models

SVM uses an epsilon-insensitive loss function to solve regression problems.

SVM Regression (SVMR) tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube. Predictions falling within epsilon distance of the true target value are not interpreted as errors.

The epsilon factor is a regularization setting for SVMR. It balances the margin of error with model robustness to achieve the best generalization to new data.

To build and test an SVMR model use a Regression Node. By default, the Regression Node tests the models that it builds; test data is created by splitting the input data into build and test subsets. You can also test a model using a Test Node. For more information about testing an SVMR model, see Testing Regression Models.