The other attributes used for decision making can be either.

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. Pruning method Pruning reduces tree size and avoids overfitting which increases the generalization performance, and thus, the prediction quality (for predictions, use the"Decision Tree Predictor" node). Available is the"Minimal Description Length" (MDL) pruning or it can also be switched off.

The target attribute must be nominal.

Reduced Error Pruning. Feb 15, Hi again! Thanks a lot for the explanation and the quick response. This was bugging me for years, and now I get it. Once again, thank you. Oct 10, Learn how to obtain the mode size of decision trees (number of leaves) to compare the complexity of different classifiers in KNIME.