Gradient Boosting Python Packages

lightgbm

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.

shap

A game theoretic approach to explain the output of any machine learning model.

catboost

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.

interpret-core

Fit interpretable models. Explain blackbox machine learning.

tensorflow-decision-forests

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

ydf

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

interpret

Fit interpretable models. Explain blackbox machine learning.

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

ngboost

Natural Gradient Boosting for Probabilistic Prediction

catboost-dev

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.

tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

lleaves

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

arboreto

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

mlrl-boomer

A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules

mlrl-common

Provides common modules to be used by different types of multi-label rule learning algorithms

forestfire-ml

Machine learning from the ground up in Rust

mlrl-seco

A scikit-learn implementation of a Separate-and-Conquer (SeCo) multi-label rule learning algorithm

mqboost

Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) and preventing quantile crossing using LightGBM and XGBoost

auto-ml

[UNMAINTAINED] Automated machine learning for analytics & production

treeplot

Plot tree based machine learning models

ydf-tf

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

torchensemble

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

xbooster

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

chefboost

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