IBM® SPSS® Decision Trees enables you to identify groups, discover relationships between them and predict future events. It features visual classification and decision trees to help you present categorical results and more clearly explain analysis to non-technical audiences. Create classification models for segmentation, stratification, prediction, data reduction and variable screening. Also, you can create models for interaction identification, category merging and discretizing continuous variables.
This module is included in the SPSS Statistics Professional edition for on premises and in the forecasting and decision trees add-on for subscription plans.
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Classifies cases into groups or predicts values of a target variable based on values of predictor variables. Enables you to predict or classify future observations based on a set of decision rules.
Includes validation tools for exploratory classification analysis. You can also view nodes using one of several methods: show bar charts of target variables, tables or both in each node.
Includes evaluation graphs to enable visual representation of gains summary tables. Provides a gains chart to identify segments by highest (and lowest) contribution.
Lets you export objects to any SPSS Statistics output format. Generate rules that define selected segments in SQL to score databases or define syntax to score SPSS Statistics files.
A fast, statistical multi-way tree algorithm that explores data quickly and builds segments and profiles with respect to the desired outcome.
A modification of the CHAID algorithm that examines all possible splits for each predictor (independent) variable.
Explore the full list of features in this module and compare features included in all SPSS Statistics editions.
A comprehensive binary tree algorithm that partitions data and produces accurate homogeneous subsets.
A statistical algorithm that selects variables without bias and builds more accurate binary trees quickly and efficiently.