Why isn't there an implementation of GBDT in Weka? (Java ML library)
Instead the recommendation is to use the MultiBoost algorithm with J48 (Java implementation of Decision Trees - C4.5 algorithm). Refer - MultiBoost

Do you know if MultiBoost is a superior version of GBDT or if there are more intricacies and differences to be aware of?

  • $\begingroup$ The first sub-question regarding the "Why isn't there an implementation of GBDT in Weka?" rests solely upon Weka's development team as they are the decision makers on what is included in their software. Consider e-mailing them. $\endgroup$
    – usεr11852
    Jun 20, 2017 at 0:28
  • $\begingroup$ @usεr11852 I was wondering if the answer has to do with the second part of the question; if in fact, multiboost is a superior version of GBDT, then perhaps that's why they don't have it in Weka $\endgroup$ Jun 20, 2017 at 3:22

1 Answer 1


I posted this question to the weka mailing list and got the following answer:

For squared error as the loss function, WEKA has AdditiveRegression. You can run it with depth-limited and otherwise unpruned REPTree models to implement gradient-boosted regression trees. You can also combine it with Bagging, and use RandomTree as an alternative to REPTree to inject randomness into the learning process.

For minimising negative loglikelihod in classification problems, you can take a look at LogitBoost as an alternative. Again, use it with depth-limited and otherwise unpruned REPTree models (or RandomTree).

To automatically determine an appropriate number of iterations for these methods, use IterativeClassifierOptimizer.


weka iterativeclassifieroptimizer


If you install the RPlugin package for WEKA, you can also access xgboost and well-known gradient boosting implementations in R through WEKA’s MLRClassifier (which uses the mlr package in R to access learning algorithms in R).


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