Skip to main content

 –

Binning predictors

Suggest edit Updated on March 11, 2021

A predictor is a field that has a predictive relationship with the outcome (the field whose behavior you want to predict). Predictors contain information about the cases whose values might show an association with the behavior you are trying to predict.

There are two types of predictors: numeric and symbolic. Numeric predictors are, for example, customer's age, income, expenditures. Symbolic predictors are, for example, customer's gender, martial status, home address.

You can tweak the treatment of predictors or allow Prediction Studio to generate a default treatment.

Binning options for numeric predictors

Binning of numeric predictors allows you to group cases into bins of equal volume or width.

For example: For example, your cases are customers that you want to group according to their age in bins of equal width. You can create bins for customers aged 20-29, 30-39, 40-49, and so on. Each bin contains a number of customers from the specific age group. When you decide to group the customers into bins of equal volume, you can create a certain number of bins and Prediction Studio divides the cases equally among the bins.
  • Binning numeric predictors

    Generate bins over which you want to distribute numeric predictors. Binning allows you to group cases into bins of equal volume or width.

  • Binning symbolic predictors

    Generate bins over which you want to distribute the symbols that are not assigned to a category.

Did you find this content helpful? YesNo

Have a question? Get answers now.

Visit the Collaboration Center to ask questions, engage in discussions, share ideas, and help others.

Ready to crush complexity?

Experience the benefits of Pega Community when you log in.

We'd prefer it if you saw us at our best.

Pega.com is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us