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
generate a default treatment.
Binning options for numeric predictors
Binning of numeric predictors allows you to group cases into bins of equal volume or
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
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.