Breast-Cancer-Diagnosis-Using-Probabilistic-Ensemble-Based-Machine-Learning-Algorithms

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.

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.

Tuning hyperparams fast with Hyperband

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

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

Real time eye tracking for embedded and mobile devices.