The program will automatically find and implement (e.g., for data marked Data for deployment) a best recoding scheme for the prediction of a continuous variable from one or more categorical predictors with many classes (e.g., such as SIC codes with over 10,000 distinct values). The program uses an efficient CHAID-like algorithm to determine the best combinations of classes that will yield a strong relationship to the respective outcome variable of interest. The recoded (aggregated) class variables (now with fewer distinct values) can then be submitted to subsequent analyses with the various tools for predictive data mining.

**General**

**Min-N to stop (% of cases)**. The minimum number of cases (observations)
per recoded class (node), expressed as a percent of the total number of
observations (if the specified percentage of cases evaluates to less than
5 observations, the minimum number of cases per recoded class (node) will
be set to 5).

**Minimum
number of categories**. Minimum number of categories to recode.

**p value
for splitting**. p value used for splitting.

**p value
for merging**. p value used for merging.

**Splitting
after merging**. Splitting after merging of categories.

**Bonferroni
adjustment**. Applies Bonferroni adjustment to probabilities.

**Add new
variables**. Add new variables to the input spreadsheet to hold
the recoded variables.