gradient-boosting

Machine learning, in numpy

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

A curated list of data mining papers about fraud detection.

[UNMAINTAINED] Automated machine learning for analytics & production

A curated list of gradient boosting research papers with implementations.

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

LAMA - automatic model creation framework

python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision Trees

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

Tuning hyperparams fast with Hyperband

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

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

Competing Risks and Survival Analysis

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch.

Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into explainable scorecards

[Mach. Learn. 2026] Official implementation of "Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework"