Viewing Predictor Details

The Select Predictor dialog box displays statistics on available predictors (or "competitors" as they are sometimes called) that can be used for the current split.

• For CHAID and exhaustive CHAID, the chi-square statistic is listed for each categorical predictor; if a predictor is a numeric range, the F statistic is shown. The chi-square statistic is a measure of how independent the target field is from the splitting field. A high chi-square statistic generally relates to a lower probability, meaning that there is less chance that the two fields are independent—an indication that the split is a good one. Degrees of freedom are also included because these take into account the fact that it is easier for a three-way split to have a large statistic and small probability than it is for a two-way split.
• For C&R Tree and QUEST, the improvement for each predictor is displayed. The greater the improvement, the greater the reduction in impurity between the parent and child nodes if that predictor is used. (A pure node is one in which all cases fall into a single target category; the lower the impurity across the tree, the better the model fits the data.) In other words, a high improvement figure generally indicates a useful split for this type of tree. The impurity measure used is specified in the tree-building node.