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tmadl/sklearn-random-rotation-ensembles: Scikit-learn compatible implementations of the Random Rotation Ensemble idea of (Blaser & Fryzlewicz, 2016)
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baidu/fast_rgf: Multi-core implementation of Regularized Greedy Forest
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scikit-learn-contrib/forest-confidence-interval: Confidence intervals for scikit-learn forest algorithms http://contrib.scikit-learn.org/forest-confidence-interval/
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patr1ckm/mvtboost: Boosted regression trees for continuous, multivariate responses.
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mirri66/xgbmagic: Pandas dataframe goes in, XGBoost model results come out
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rotationsymmetry/sparkxgboost: Gradient boosting tree of arbitrary user-defined loss function
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glouppe/talk-pydata2015: Talk on "Tree models with Scikit-Learn: Great learners with little assumptions" presented at PyPata Paris 2015
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cstraehl/ljlearn: decision tree algorithms for lua + luajit
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dmlc/xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, C++ and more
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MLWave/Black-Boxxy: Some experiments into explaining complex black box ensemble predictions.
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Far0n/xgbfi: XGBoost Feature Interactions & Importance
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