SVM (support vector machine) for regression

Support Vector Machine (SVM) for regression predicts continuous ordered variables based on the training data.

Unlike Logistic regression, which you use to determine a binary classification outcome, SVM for regression is primarily used to predict continuous numerical outcomes.

You can use the following functions to build an SVM for regression model, view the model, and use the model to make predictions on a set of test data:

• SVM_REGRESSOR
• PREDICT_SVM_REGRESSOR
• GET_MODEL_SUMMARY

For a complete example of how to use the SVM algorithm in Vertica, see Building an SVM for regression model.