#Gradient-boosting
Showing 31 of 31 repositories tagged #gradient-boosting, ranked by stars
A game theoretic approach to explain the output of any machine learning model.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Machine learning, in numpy
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Fit interpretable models. Explain blackbox machine learning.
A collection of research papers on decision, classification and regression trees with implementations.
Natural Gradient Boosting for Probabilistic Prediction
A curated list of data mining papers about fraud detection.
[UNMAINTAINED] Automated machine learning for analytics & production
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
Perpetual is a high-performance gradient boosting machine. It delivers optimal accuracy in a single run without complex tuning through a simple budget parameter. It features out-of-the-box support for causal ML, continual learning, native calibration, and robust drift monitoring, along with Rust core and zero-copy bindings for Python and R
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.
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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.
Machine Learning Roadmap for 2025. Step-by-step guide to become a Data Scientist. Covers the best free learning resources from Python basics to Deep Learning and MLOps.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Competing Risks and Survival Analysis
TigerLily: Finding drug interactions in silico with the Graph.
An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.
Machine learning regression algorithm on cryptocurrency stock price for the next 30 days.
The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch.
A collection of boosting algorithms written in Rust 🦀
An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.
Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset