szilard/awesome-GBMs

A curated list of gradient boosting machines (GBM) resources

Awesome Gradient Boosting Machines (GBMs)

A curated list of GBM resources. This document is public domain (all copyright waved hereby). You can submit a pull request if you'd like to contribute.

Algorithm description (original algo)

• Wikipedia
• Friedman's original paper
• Hastie, Tibshirani, Friedman's ESL2 book, Chap 10

Implementations

xgboost

• KDD paper by the authors / Tianqi Chen
• A gentle introduction by machinelearningmastery.com

h2o

• H2O GBM Booklet

lightgbm

• NIPS paper by the authors / Guolin Ke

catboost

• NIPS paper by the authors / Yandex

sklearn

• Talk slides by contributors Peter Prettenhofer and Gilles Louppe

R gbm package

• Guide by the package author / Greg Ridgeway

Spark MLlib

• Introductory blog post by the authors / Databricks

GBM performance/scaling

• Speed benchmark (CPU/GPU) (top libraries) by Szilard Pafka
• Speed, RAM usage (CPU only) (more libraries) by Szilard Pafka
• xgboost+lightgbm multithread scaling benchmarks by Damien Soukhavong aka Laurae

GBM tuning

• GBM tuning by machinelearningmastery.com
• H2O GBM tuning tutorial by Arno Candel
• GBM tuning by analyticsvidhya.com

Kaggle