A curated list of gradient boosting research papers with implementations.

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

Python版OpenCVのTracking APIの比較サンプル

Spells for everyday living, also a book -- Models Demystified -- coming out in 2025.

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]

[OPEN teaching project] The transfer learning code for understanding and teaching : Boosting for transfer learning with single / multiple source(s)

A face detection program in python using Viola-Jones algorithm.

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

Functional gradient boosting based on residual network perception

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

We got a stew going!

An implementation of the paper "A Short Introduction to Boosting"

MILBoost and other boosting algorithms, compatible with scikit-learn

Code repository of the paper "BooVAE: Boosting Approach for Continual Learning of VAE" published at NeurIPS 2021. https://arxiv.org/abs/1908.11853

LogitBoost classification algorithm built on top of scikit-learn

A boosting procedure for multitask learning on graph-structured data

A simplified implement of Adaptive boosting.

Open source gradient boosting library