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Best New Features in SAP Predictive Analytics 2.2

Best New Features in SAP Predictive Analytics 2.2

SAP released Predictive Analytics 2.2 on June 12. This version has several important new features on the Expert Analytics side which more advanced users will find very interesting.

Installation Issue

Installing Predictive Analytics 2.2 requires fully uninstalling version 2.1. In addition, users that have previously installed 2.1 or a prior version and used local R algorithms may have problems configuring R; when going to the “Configure and Install R” window, the checkbox will re-set every time it is opened so R algorithms will remain grayed out. This SCN discussion provides the fix – renaming the .sappa folder in the User directory.

Model Comparison Tools

The most important new features in this version is the ability to objectively compare the results of classification and regression models side-by-side. Two new components have been added on the “Preprocessors” menu under “Data Preparation” algorithms. Perhaps these will move eventually because these algorithms are used post-modeling, not before. The “Model Statistics” component generates 2 model metrics: KI (a measure of model accuracy to the actual values) and KR (a measure of model repeatability on multiple samples.) Both KI and KR run on a scale of 0 to 1, with a 1 for both KI and KR being a “perfectly” predictive model. KI tends to be much lower than KR for many use cases; generally a model with a KR of <0.9 would not be robust enough to consider using, but KI is relative to the use case, so sometimes a 0.6 is very good (especially with behavior prediction).

Predictive 2.2 (1)Predictive 2.2 (2)


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Hilary Bliss About Hillary Bliss
Hillary Bliss is a Senior ETL Consultant at Decision First Technologies, and  specializes in data warehouse design, ETL development, statistical analysis, and  predictive modeling. She works with clients and vendors to integrate business  analysis and predictive modeling solutions into the organizational data  warehouse and business intelligence environments based on their specific operational and strategic business needs. She has a master’s degree in statistics and an MBA from Georgia Tech.


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