nestedhyperboost

A game theoretic approach to explain the output of any machine learning model.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Automated Machine Learning with scikit-learn

A modular active learning framework for Python

Natural Gradient Boosting for Probabilistic Prediction

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

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