A collection of research papers on decision, classification and regression trees with implementations.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

A curated list of gradient boosting research papers with implementations.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Machine learning for C# .Net

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Tiny Gradient Boosting Tree

Performance of various open source GBM implementations

Building Decision Trees From Scratch In Python

Gradient Boosting powered by GPU(NVIDIA CUDA)

PKBoost: Adaptive GBDT for Concept Drift, Built from scratch in Rust, PKBoost manages changing data distributions in fraud detection with a fraud rate of 0.2%. It shows less than 2% degradation under drift. In comparison, XGBoost experiences a 31.8% drop and LightGBM a 42.5% drop

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

A collection of boosting algorithms written in Rust 🦀

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Programmable Decision Tree Framework

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).

mlim: single and multiple imputation with automated machine learning

NTUEE Machine Learning, 2017 Spring