What it can do for your business
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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Feature spotlights
Tree classification model
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.
Validation and analysis
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.
Evaluation features
Includes evaluation graphs to enable visual representation of gains summary tables. Provides a gains chart to identify segments by highest (and lowest) contribution.
Export capabilities
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.
CHAID algorithm
A fast, statistical multi-way tree algorithm that explores data quickly and builds segments and profiles with respect to the desired outcome.
Exhausted CHAID algorithm
A modification of the CHAID algorithm that examines all possible splits for each predictor (independent) variable.
Access complete list of features
Explore the full list of features in this module and compare features included in all SPSS Statistics editions.
Classification and regression tree algorithm
A comprehensive binary tree algorithm that partitions data and produces accurate homogeneous subsets.
QUEST algorithm
A statistical algorithm that selects variables without bias and builds more accurate binary trees quickly and efficiently.
Product images
Model summary table
Dependent and Independent variable selection
Tree diagram
Output Tree settings
Technical details
Software requirements
- For on premises: Purchase the Professional edition
- For subscription plans: Purchase the forecasting and decision trees add-on
Hardware requirements
- Processor: 2 GHz or faster
- Display: 1024*768 or higher
- Memory: 4 GB of RAM required, 8 GB of RAM or more recommended
- Disk space: 2 GB or more