Decision Tree Learner

This node induces a classification decision tree in main memory. The target attribute must be nominal. The other attributes used for decision making can be either nominal or numerical. Numeric splits are always binary (two outcomes), dividing the domain in two partitions at a given split point. Nominal splits can be either binary (two outcomes) or they can have as many outcomes as nominal values. In the case of a binary split the nominal values are divided into two subsets. The algorithm provides two quality measures for split calculation; the gini index and the gain ratio. Further, there exist a post pruning method to reduce the tree size and increase prediction accuracy. The pruning method is based on the minimum description length principle.
The algorithm can be run in multiple threads, and thus, exploit multiple processors or cores.
Most of the techniques used in this decision tree implementation can be found in "C4.5 Programs for machine learning", by J.R. Quinlan and in "SPRINT: A Scalable Parallel Classifier for Data Mining", by J. Shafer, R. Agrawal, M. Mehta ( http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.104.152&rep=rep1&type=pdf )

Input ports

1. Type: Table
   Input data
   The pre-classified data that should be used to induce the decision tree. At least one attribute must be nominal.

Output ports

1. Type: PMML
   Decision Tree Model
   The induced decision tree. The model can be used to classify data with unknown target (class) attribute. To do so, connect the model out port to the "Decision Tree Predictor" node.

External resources

• KNIME E-Learning Course: Decision Tree