Home > Data Mining Algorithms > Anomaly Detection > Anomaly Detection Viewers a... > Association > AR Model Viewers and Algori... > Decision Tree > Expectation Maximization > EM Model Viewer and Algorit... > Generalized Linear Models > GLM Model Viewers and Algor... > k-Means > Naive Bayes > Nonnegative Matrix Factoriz... > Orthogonal Partitioning Clu... > Singular Value Decompositio... > Support Vector Machine > Building and Testing SVM Mo... > 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.