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Average Square Error Decision Tree

Logworth Decision Tree
Sas Enterprise Miner Decision Tree Tutorial
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StatementSCORE StatementTARGET Statement Details Building a TreeInterval Input Binning DetailsInput Variable Splitting and SelectionPruningMemory ConsiderationsHandling Missing ValuesHandling Unknown Levels in ScoringSplitting CriteriaPruning CriteriaSubtree StatisticsVariable ImportanceOutputs Examples Creating a Node logworth definition Rules Description of a TreeAssessing Variable Importance References Pruning Criteria Subsections:

Logworth Decision Tree

Decision Tree Entropy Pruning Criterion Decision Tree Gini Pruning Criterion Decision Tree Misclassification Rate Pruning Criterion

Sas Enterprise Miner Decision Tree Tutorial

Decision Tree Average Square Error Pruning Criterion Regression Tree Average Square Error Pruning Criterion Pruning criteria are similar to growth criteria, except that they use the

Logworth Calculation

global change of a metric instead of the per-leaf change. In addition, partition is present, pruning statistics are calculated from the validation partition if one is present. Decision Tree Entropy Pruning Criterion When you prune by entropy, the entropy is calculated as though the entire data set were a single leaf that is logworth jmp partitioned into the final number of leaves. Thus, it can be expected that the path taken during pruning might not correspond to the reverse of the path taken during growth, even if the pruning and growth metrics are identical. The change is then based on the global entropy, comparing the entropy when node is preserved to the entropy when the node is pruned back to a leaf. Decision Tree Gini Pruning Criterion As with entropy, the change in Gini statistic is calculated based on the change in the global Gini statistic. The equations for this criterion are otherwise identical to the equations shown in the section Gini Splitting Criterion. Decision Tree Misclassification Rate Pruning Criterion The misclassification rate (MISC) is simply the number of mispredictions divided by the number of predictions. Thus, for a leaf that has a predicted target level , the misclassification rate is For all the leaves in the tree, it is The predicted target

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