A collection of research papers on decision, classification and regression trees with implementations.
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Updated
Mar 16, 2024 - Python
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
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http:https://user2016.org/tutorials/10.html
Machine learning for C# .Net
Tiny Gradient Boosting Tree
Performance of various open source GBM implementations
Gradient Boosting powered by GPU(NVIDIA CUDA)
Building Decision Trees From Scratch In Python
Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)
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).
NTUEE Machine Learning, 2017 Spring
mlim: single and multiple imputation with automated machine learning
A collection of boosting algorithms written in Rust 🦀
Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.
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