PMML (Predictive Model Markup Language) provides a standard way to represent data mining models so that these can be shared between different statistical applications. Not only PMML can represent a wide range of statistical techniques, but it can also be used to represent the data transformations necessary to transform raw data into meaningful feature detectors. In this way, PMML offers a standard to represent data manipulation and modeling in a single and concise way.

1. PMML Overview

5. PMML Files 3) Post-Processing Scaling of model outputs can be performed with PMML element Targets 1) Pre-Processing PMML elements Transformations, Mining Schema and Functions allow for effective pre-processing 2) Models PMML allows for several predictive modeling techniques to be fully expressed PMML

7. Data Pre-Processing: PMML Example Arbitrary Piecewise Linear Function This PMML code implements: Var_b:=interpolate(Var_a,((100,0),(200,1),(800,3),(900,4))) See http://www.dmg.org/v3-2/Transformations.html - look for element NormContinuous.

9. Modeling Elements: PMML Example for Neural Network

10. The PMML code below implements score post-processing. It uses the PMML element Targets for checking boundaries ( min and max ) and to rescale ( rescaleConstant and rescaleFactor ) the original score generated by model See http://www.dmg.org/v3-2/Targets.html Data Post-Processing: PMML Example 3

11. Applications Service Providers External Vendors Divisions One Standard, One Process

12. PMML = Easy Model Deployment Model Deployment Model Building PMML

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