Pruning

The Pruning property of the decision tree visualization determines how aggressively your decision tree is pruned. The growth algorithm creates a decision tree based on the properties that you specify. The pruning algorithm considers each node to be a root node of its own subtree, starting from the bottom. If the misclassification rate of the subtree is significantly better than the misclassification rate of the root node, then the subtree is kept. If the misclassification rate of the subtree is similar to the misclassification rate of the root node, then the subtree is pruned. In general, smaller decision trees are preferred.

If the Pruning property slider is closer to Lenient, then the difference in the misclassification rates must be relatively small. If the Pruning property is closer to Aggressive, then the difference in the misclassification rates must be relatively large. That is, a lenient pruning algorithm allows the decision tree to grow much deeper than an aggressive pruning algorithm.

Variables that are not used in any split can still affect the decision tree, typically due to one of two reasons. It is possible for a variable to be used in a split, but the subtree that contained that split might have been pruned. Alternatively, the variable might include missing values, but the Include missing property is disabled.