DGBM

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.

A python library for decision tree visualization and model interpretation.

For extensive instructor led learning

Natural Gradient Boosting for Probabilistic Prediction

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.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

An extension of XGBoost to probabilistic modelling