Library for gradient boosting tree

Question

Do you know of a good library for gradient boosting tree machine learning?

preferably:

• with good algorithms such as AdaBoost, TreeBoost, AnyBoost, LogitBoost, etc
• with configurable weak classifiers
• capable of both classification and prediction (regression)
• with all kinds of allowed signals: numbers, categories or free text
• C/C++ or Python
• opensource

So far I have found http://www.multiboost.org/home which looks good. But I wonder if there are other libraries?

Answer 1

If you're looking for a python version, the latest release of scikit-learn features gradient boosted regression trees for classification and regression (docs).

It is similar to R's gbm package - gbm is faster for (least-squares) regression wheres scikit-learn's implementation is faster at test-time and when your number of features > 1000.

Answer 2

These don't neccessarily meet all your preferences, but there's also:

• Treenet a commercialization and extension of Jerome Friedman's original implementation. Not open source but we've found it to work pretty well
• R gbm package for gradient boosted trees specifically.