Table of Contents

Supported PMML model types

The Pega 7 Platform uses a specific implementation of the PMML format, which means that some of the PMML features and models are not supported in the Predictive Model rule. PMML developers should know the supported PMML versions and models, as well as the unsupported models and features, when building PMML models for the Pega 7 Platform. Knowing these limitations prevents issues that might occur when you upload the PMML file.

Supported PMML versions

  • 3.0
  • 3.1
  • 3.2
  • 4.0
  • 4.1
  • 4.2
  • 4.2.1
  • 4.3

Supported models

  • Cluster model
  • General regression
  • Neural network
  • k-nearest neighbors
  • Naive Bayes
  • Ruleset
  • Regression
  • Support vector machine
  • Scorecard
  • Decision Tree
  • Ensemble models (including Random Forest and Gradient Boosting)

Unsupported models

  • Association rules
  • Base line models
  • Ensemble (Mining/Many-in-one) models that contain composite embedded models
  • Sequences
  • Text
  • Time series

Unsupported features

  • Cluster models
    • The kind attribute of the ComparisonMeasure element can be set to distance or similarity.
  • General regression
    • If the functionName attribute of the GeneralRegressionModel element is regression, the model must have exactly one PPMatrix.
    • The multinomialLogistic, ordinalMultinomial, and CoxRegression algorithms are not supported for the regression mining function.
    • The regression, general_linear, and CoxRegression algorithms are not supported for the classification mining function.
  • k-nearest neighbors
    • The opType attribute of the input field (DataField) can be set to continuous or categorical.
    • The kind attribute of the ComparisonMeasure element can be set to distance or similarity.
  • Naive Bayes - Supports only one classification mining function.
  • Neural network - The mining function can be regression or classification.
  • Regression
    • If the functionName attribute of the RegressionModel element has the value regression, the normalizationMethod attribute can have one of the following values: none, softmax, logit, exp.
    • If the functionName attribute of the RegressionModel element has the value classification, the normalizationMethod attribute can have one of the following values: none, softmax, logit, loglog, cloglog.
    • Scorecard
      • The functionName attribute is mandatory for the ScorecardModel element.
    • Support vector machine
      • The svmRepresentation attribute is mandatory for the SupportVectorMachineModel element.
      • The functionName attribute for the SupportVectorMachineModel element cannot be empty and has to be set to regression or classification.
      • The probability attribute value is not supported for the resultFeature attribute in the Output element.
    • Tree
      • The functionName attribute for the TreeModel element cannot be empty, and has to be set to regression or classification.
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