Reference Data Guide

10.5.6
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- 10.5.8
- 10.5.7
- 10.5.1
- 10.5
- 10.4.1
- 10.4.0
- 10.2.2

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Probabilistic Model Configuration

The steps to configure a probabilistic model begin with the type of analysis that you want to perform. Use a probabilistic model in a Labeler transformation to identify the types of information in each value in an input string. Use a probabilistic model in a Parser transformation to parse the data values in an input string to different output ports.

You can use the same probabilistic model to label data and to parse data. When you use the model in a Labeler transformation, the transformation creates a single output port for each input port that you select. When you use the model in a Parser transformation, the transformation creates an output port for each type of input data that it identifies.

To create a probabilistic model, perform the following tasks:

Identify the reference data values and the label values to add to the model.

You can use a fragment of the data that you want to analyze. Create a data object in the Model repository that reads the data fragment.

Create a content set, and add a probabilistic model to the content set.

Add the reference data values to the model.

Add the label values to the model.

You can import the data from the data object in the Model repository. You can also enter a single row of reference data or a single label.

To use the probabilistic model to parse data, verify that the model contains a label value for each output port that the transformation must create.

Assign a label to reference each data value in each row.

You can assign a label to multiple reference data values in a single operation.

Compile the model.

After you compile the probabilistic model, you can use the model in a transformation.