PASW Decision Trees
Easily Identify Groups and Predict Outcomes
The PASW Decision Trees module (formerly called SPSS Decision Trees) creates classification and decision trees to help you better identify groups, discover relationships between groups, and predict future events.
This module features highly visual classification and decision trees. These trees enable you to present categorical results in an intuitive manner, so you
can more clearly explain categorical results to non-technical audiences. PASW Decision Trees enables you to explore results and visually determine
how your model flows. This helps you find specific subgroups and relationships that you might not uncover using more traditional statistics. PASW Decision Trees includes four established tree-growing algorithms.
Use PASW Decision Trees in a variety of applications in which you need to identify groups. These applications include:
- Database marketing
- Market research
- Credit risk scoring
- Program targeting
- Marketing in the public sector
Data Analysis
Gain Specialized Techniques for Classification
The PASW Decision Trees module provides specialized tree-building techniques for classification—entirely within the PASW Statistics environment.
PASW Decision Trees includes four established tree-growing algorithms:
- CHAID: A fast, statistical, multi-way tree algorithm that explores data quickly and efficiently, and builds segments and profiles with respect to the desired outcome
- Exhaustive CHAID: A modification of CHAID, which examines all possible splits for each predictor
- Classification and regression trees (C&RT): A complete binary tree algorithm, which partitions data and produces accurate homogeneous subsets
- QUEST: A statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently
With four algorithms, you have the ability to try different types of tree-growing algorithms and find the one that best fits your data.
Because you create classification trees directly within PASW Statistics, you can conveniently use the information that results to segment and group cases
directly within the data. Additionally, you can generate selection or classification/prediction rules in the form of PASW Statistics syntax, SQL
statements, or simple text (through syntax). You can display these rules in the Viewer and save them to an external file for later use to make predictions
about individual and new cases. If you'd like to use your results to score other data files, you can write information from the tree model directly to your data or create XML models for use in PASW Statistics Server.
Back to PASW Statistics Base page.
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