aws-machine-learning-university-dte

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

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

AI比赛相关信息汇总

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

Combining tree-boosting with Gaussian process and mixed effects models

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks