Coach-QD-MARL

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.

A collection of research papers and software related to explainability in graph machine learning.

For extensive instructor led learning

Interpretability and explainability of data and machine learning models

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

Explainability for Vision Transformers