SVM (support vector machine) for classification

Support Vector Machine (SVM) is a classification algorithm that assigns data to one category or the other based on the training data. This algorithm implements linear SVM, which is highly scalable.

You can use the following functions to train the SVM model, and use the model to make predictions on a set of test data:

• SVM_CLASSIFIER
• PREDICT_SVM_CLASSIFIER
• GET_MODEL_SUMMARY

You can also use the following evaluation functions to gain further insights:

• CONFUSION_MATRIX
• DETECT_OUTLIERS
• ERROR_RATE
• ROC

For a complete example of how to use the SVM algorithm in Vertica, see Classifying data using SVM (support vector machine).

The implementation of the SVM algorithm in Vertica is based on the paper Distributed Newton Methods for Regularized Logistic Regression.