Decision Trees helps you better identify groups, discover relationships between them and predict future events. This module features highly visual classification and decision trees that enable you to present categorical results in an intuitive manner, so you can more clearly explain categorical analysis to non-technical audiences. It includes four tree-growing algorithms, giving you the ability to try different types and find the one that best fits your data.quickly and easily diagnose your missing data and fill in the blanks to create higher-value data which result in better models. When you ignore or exclude missing data, you risk reaching invalid and insignificant results.
The module provides specialized tree-building techniques for classification within the IBM SPSS Statistics environment. The four tree-growing algorithms include:
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 that partitions data and produces accurate homogeneous subsets.
QUEST — a statistical algorithm that selects variables without bias and builds accurate binary trees quickly and efficiently.