Exercise on classification with decision tree.
The pruning method is based on the minimum description length principle.
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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.
Reduced Error Pruning. Feb 15, Hi again! Thanks a lot for the explanation and the quick response.
Whatever machine learning algorithm you choose, you always need to train it and evaluate it.
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.