CHAID first examines the crosstabulations between each of the input fields and the outcome, and tests for significance, using a chi-square independence test for categorical targets or an F test for scale targets. If more than one of these relations is statistically significant, CHAID will select the input field that is the most significant (smallest p value). If an input has more than two categories, these are compared, and categories that show no differences in the outcome are collapsed together. This is done by successively joining the pair of categories showing the least significant difference. This category-merging process stops when all remaining categories differ at the specified testing level. For nominal input fields, any categories can be merged; for an ordinal set, only contiguous categories can be merged.
Exhaustive CHAID is a modification of CHAID developed to address some of the weaknesses
of the CHAID method. In particular, sometimes CHAID may not find the optimal split for a variable, since it stops merging categories as soon as it finds that all remaining categories are statistically different. Exhaustive CHAID remedies this by continuing to merge categories of the predictor variable until only two super-categories are left. It then examines the series of merges for the predictor and finds the set of categories that gives the strongest association with the target variable, and computes an adjusted p-value for that association. Thus, Exhaustive CHAID can find the best split for each predictor, and then choose which predictor to split on by comparing the adjusted p-values.
Exhaustive CHAID is identical to CHAID in the statistical tests it uses and in the way it treats
missing values. Because its method of combining categories of variables is more thorough than
that of CHAID, it takes longer to compute. However, if you have the time to spare, Exhaustive
CHAID is generally safer to use than CHAID. It often finds more useful splits, though depending
on your data, you may find no difference between Exhaustive CHAID and CHAID results.
CHAID methods are popular in large part due to their flexibility. Unlike some other tree methods, CHAID methods can generate non-binary trees, meaning that some splits have more than two branches. It therefore tends to create a wider tree than the binary growing methods. CHAID methods work for all types of inputs, and it accepts both analysis and frequency weights.
Like your visualization? Why not deploy it? For more information, see Deploy a model.