Create a model that works well on mid-volume, highly non-linear data.
In the Model creationstep, click New Decision Tree.
In the Decision tree model dialog box, select predictors.
Click Use best of each group to select the best predictors in a group.
Click Use all predictors to select all the predictors.
Select or clear the check boxes in the Use column to manually select predictors.
From the Select decision tree parameters section, select one of the splitting methods:
CHAID- Selects the most statistically significant point to split as measured by the Chi Squared statistic. In the Significance is over spin box, set the minimum level of significance for splitting.
CART- Selects the point to split that has the lowest impurity (the lowest level of cases on the wrong side of the split). In the Impurity is under spin box, set the maximum level of impurity for splitting. Raising the value can generate larger models.
ID3- Selects the point to split that has the highest information value described by the entropy of the distribution or gain. In the Gain is over spin box, set the minimum level of gain for splitting. Lowering the value can generate larger models.
Set the maximum depth of the node tree and the minimum size of the leaf.
Maximum depth- The maximum distance measured in the number of ancestors from a leaf to the root.
Minimum leaf size- The minimum size of a leaf as a percentage of the sample.
The greater the depth and the smaller the minimum, the more specific the predictions can be. However, they can also become less reliable.
Click Create model.
Click Save model.
Click Submit.