Initializing help system before first use

Identifying Variables in Decision Trees

Xpress Insight identifies variables in a unique way specific to decision trees.

The following table lists each data type available in decision trees and how it is used in Xpress Insight.
Variables in Decision Trees
Data Type Identification Criteria Inserting Splits Target-Driven Decision Trees Statistics for Profiling Variables

real

All numeric variables

Enter branch thresholds.

Best Split algorithm is supported.

  • Categorical—Numeric variables with 10 or fewer unique values
  • Continuous—Numeric variables with more than 10 unique values

enum (enumeration)

All string variables with 100 or fewer unique values

All unique values are automatically added to the tree after you click APPLY. Each value appears as its own node in the tree.

Best Split algorithm is not supported.

Categorical

string

All string variables with more than 100 unique values

Each unique value must be entered manually as a branch value.

Best Split algorithm is not supported.

Not available as a profile variable.
Tip If you need to profile this variable, complete the following steps:
  1. Create a wrangler using the same dataset.
  2. Copy this variable and then modify it so there are 100 or fewer unique values.
  3. Publish the (full) dataset.
  4. Create a new tree using the newly published dataset. Add the wrangled variable as a profile variable.
Tip You can verify the data type of a variable in a decision tree by right-clicking its level and selecting Properties to open its Properties dialog box.
Parent Topic Profiling Decision Trees