Gradient Boosting


Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

Overview

XGBoost Algorithm: Long May She Reign
A closer look at XGBoost and why it performs so well on structured data.
gradient-boosting xgboost tutorial article

Libraries

General
Scikit-learn
Examples for all the different utilities within scikit-learn.
scikit-learn naive-bayes linear-regression logistic-regression
XGBoost: eXtreme Gradient Boosting
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, ...
gradient-boosting xgboost code library
CatBoost
A high-performance open source library for gradient boosting on decision trees
gradient-boosting decision-trees catboost library
LightGBM - Light Gradient Boosting Machine
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms.
gradient-boosting decision-trees lightgbm library
Table of Contents
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