Apart from creating your own models from Pega Platform™ templates, you can import predictive models in the PMML
format created in third-party tools to predict customer actions.
The PMML
supported models are:
- Clustering
- GeneralRegressionModel
- MiningModel
- NaiveBayesModel
- NearestNeighborModel
- NeuralNetwork
- RegressionModel
- RuleSetModel
- Scorecard
- SupportVectorMachineModel
- TreeModel
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In the navigation panel of Prediction Studio, click Predictions.
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In the header of the Predictions work
area, click .
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In the New predictive model dialog box, enter a
Name for your model.
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In the Create model section, click Import
PMML.
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Click Choose and select a model file to upload.
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In the Save model section, specify the context where you want to
save the model:
If |
Then |
If you want to save your model in the default application context, |
select the Use default context check box. For more
information, see Configuring the default rule context.
|
If you want to save your model in a custom context, |
specify the context details:
- Place the cursor in the Apply to class field, press the
Down arrow key, and click the class in which you want to save the model.
- Define the class context by selecting appropriate values from the
Development branch, Add to
ruleset, and Ruleset version lists.
|
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Verify the settings and click Next.
- Optional:
In the Outcome definition section, change the default label for
the model objective by clicking Set labels and entering a
meaningful name in the associated field.
To enable response capture, the model objective label must be the same as the
.pyPrediction parameter value in the response strategy (applies to
all model types).
-
In the Outcome definition section, specify what the model
predicts:
To enable response capture for binary models, the label of the predicted outcome that
you want to monitor must be the same as the .pyOutcome parameter value
in the response strategy.
If |
Then |
If you are importing a binary (scoring) model, |
perform the following actions:
- In the Monitor the probability of field, select the
outcome that you want to predict.
- In the Advanced section, enter the expected score
range.
- In the Classification output field, select one of the
model outputs to classify the model.
|
If you are importing a special binary (scoring) model to predict a
number, |
perform the following actions:
- In the Predicting list, select continuous
value.
- In the Predicting values between fields, enter the range
of outcome values that you want to predict.
|
If you are importing a categorical (multi-class) model, |
in the Predicting section, verify the categories to
predict. |
If you are importing a continuous model, |
in the Predicting values between fields, enter the range
of outcome values that you want to predict. |
-
In the Expected performance field, enter a value that represents
the expected predictive performance of the model.
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Confirm the PMML model settings by clicking Import.
Result: Your custom model is now available in Pega Platform.
What to do next: Configure your PMML model. For more information, see Configuring a PMML model.