The new SPSS Decision Trees add-on module creates classification and decision trees directly within SPSS to help you better identify groups, discover relationships between groups, and predict future Evenementen. You can use classification and decision trees for segmentation, stratification, prediction, data reduction and variable screening, interaction identification, category merging, and discretizing continuous variables.
Highly visual trees enable you to present categorical results in an intuitive manner—so you can more clearly explain categorical results to non-technical audiences. These trees enable you to explore your results and visually determine how your model flows. Visual results can help you find specific subgroups and relationships that you might not uncover using more traditional statistics. Because classification trees break down the data into branches and nodes, you can easily see where a group splits and terminates.
SPSS Decision Trees diagrams, tables, and graphs
are easy to interpret. Use the highly visual trees to discover relationships
that are currently hidden in your data (top). Use tree model results to score
cases directly in SPSS (bottom).
Choose from four tree-growing algorithms SPSS 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 & regression trees (CRT)—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.
Since you use SPSS Decision Trees within the SPSS interface, you can create classification trees directly within SPSS and conveniently use the information that results to segment and group cases directly within the data. There is no back and forth between SPSS and other software. Additionally, you can generate selection or classification/prediction rules in the form of SPSS 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 SPSS Server.
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