Since regression functions can only be fit to numeric attributes, LMT.

Sep 22, Data Mining with Weka: online course from the University of WaikatoClass 3 - Lesson 5: Pruning decision treesbushleaning.bar (PDF): https://. Jul 20, The unpruned trees are larger.

There are some simple techniques for pruning, and some more complicated techniques for pruning.

What happens is that basically the tree is created according to the implemented algorithm and if pruning is enabled, an additional step looks at what nodes/branches can be removed without affecting the performance too much.

The idea behind pruning is that, apart from making the tree easier to understand, you reduce the risk of overfitting to the training. Aug 08, Decision trees run the risk mango tree flower drop overfitting the training data. One simple counter-measure is to stop splitting when the nodes get small. Another is to construct a tree and then prune it back, starting at the leaves. For this, J48 uses a statistical test which is rather unprincipled but works well.5/5(56).

Jun 05, Download WEKA - DecisionTree - ID3 with Pruning for free. The Decision Tree Learning algorithm ID3 extended with pre-pruning for WEKA, the free open-source Java API for Machine Learning.

It achieves better accuracy than WEKA's ID3, which lacks bushleaning.bar: bushleaning.bar(1).

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Sep 25, RandomTree is a Weka specific reference to a single tree of a RandomForest. This equals random feature selection at each node in weka tree pruning one tree on the entire data set. Reduced-Error Pruning Decision Tree (REPTree) REPTree (Reduced-Error Pruning) is another algorithm that is specific to Weka. It is a fast decision tree learner that is optimised for simplicity and bushleaning.bars: 1.