Watson Studio

Linear Support Vector Machine in Modeler 17.1

By Armand Ruiz posted Mon October 12, 2015 03:13 PM

Today let's  introduce Linear Support Vector Machine (LSVM),  another new algorithm included with IBM SPSS Modeler 17.1. This algorithm is also available from the Modeling Palette and it is particularly suited for use with wide datasets, that is, those with a large number of predictor fields. It is a supervised learning model that analyze data and recognize patterns and it is used for classification and regression analysis.  You can use the default settings on the node to produce a basic model relatively quickly, or you can use the build options to experiment with different settings.

The LSVM node enables you to use a linear support vector machine to classify data and it supports:

  • Binary, Categorical and Numeric targets

  • L1/L2 regularization

  • PMML generation

  • and is scoreable via the database Scoring Adapters

It is similar to Support Vector Machine (SVM) but it can scale better to large number of records. LSVM node runs when connected to the IBM SPSS Analytic Server.



Learn more about LSVM here: SPSS LSVM