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IBM SPSS Modeler 18.1 -- Coding Free Open Source, Seamless Weather Data and Optimization Integration and Text Analytics for Big Data

Today I am happy to announce the release of new versions of the main products in the IBM SPSS Data Science portfolio -- IBM SPSS Modeler 18.1, IBM SPSS Collaboration and Deployment Services 8.1, and IBM SPSS Analytic Server 3.1. This new release presents six major categories of improvements: ...

Ted Fischer's profile image

Blog Entry
Spark + SPSS Modeler: Boosted Trees, K-Means, and Naive Bayes

We are excited to announce the release of 3 new extensions for SPSS Modeler using MLlib implemented algorithms and PySpark. These three extensions are Gradient-Boosted Trees, K-Means Clustering, and Multinomial Naive Bayes. Niall McCarroll, IBM SPSS Analytic Server Software Engineer, and I ...

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Announcing the new IBM SPSS Deep Dive Webinar Series

[caption id="attachment 4578" align="alignleft" width="300"] Explore the new IBM SPSS Deep Dive Webinar Series[/caption] The recent new releases of IBM SPSS Modeler and IBM SPSS Statistics offer many compelling enhancements and capabilities that will excite longtime and new users alike. Our...

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Announcing IBM SPSS Modeler 18

Today we are releasing Modeler version 18. There a quite a number of important changes and improvements in this version. We have four groupings of changes – Big Data Algorithms in Modeler, changes that continue Extend and Embrace the Value of Open Source, Platform Flexibility and other changes...

Ted Fischer's profile image

Blog Entry
Coding a Python/Spark Modeler Extension for Collaborative Filtering

Introduction IBM SPSS Modeler 17.1 introduces the ability to build extension nodes written in the Python2 language and leveraging Apache Spark via the Python for Spark (pyspark) API . This feature is introduced in an earlier post . To run extension nodes built in python, Modeler needs...

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SPSS Extends Spark Support to Cloudera & MapR

IBM this week released an update to IBM SPSS Analytic Server 2.1 that extends SPSS’ support for Apache Spark to all four leading commercial software distributions based on Apache Hadoop. Analytic Server 2.1.0.1 builds on the Spark integration first released in late September by adding support...

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Spark integration in SPSS Modeler 17.1

In the new release of IBM SPSS Modeler 17.1 we introduced integration with Apache Spark. In this post I will explain more about this integration and why it is so powerful. Why Should Customers Care about Apache Spark? Complex workloads complete significantly faster in Spark compared Hadoop...

Armand Ruiz's profile image

Blog Entry
Run decision trees on Big Data

To close these series of posts about the new algorithms of IBM SPSS Modeler 17.1, today is the turn of Tree-AS. The Tree-AS node can be used with data in a distributed environment to build CHAID decision trees using chi-square statistics to identify optimal splits. The pre-existing tree...

Armand Ruiz's profile image

Blog Entry
Generalized Linear Engine and Linear AS

Today I'm going to introduce two new algorithms of IBM SPSS Modeler 17.1: GLE and Linear-AS Generalized Linear Engine (GLE) GLE provides a variety of statistical models such as linear regression for normally distributed responses, logistic models for binary data, log linear models for count...

Armand Ruiz's profile image

Blog Entry
Linear Support Vector Machine in Modeler 17.1

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...

Armand Ruiz's profile image