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.

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

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

Gradient Boosting powered by GPU(NVIDIA CUDA)

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

Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors