Ensemble learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone (Wiki)

Boosting (Wiki

• The Strength of Weak Learnability (1990) Robert Schapire
• Arcing the edge (1997) Leo Breiman
• A Short Introduction to Boosting (1999) Yoav Freund, Robert E. Schapire
• Boosting Algorithms as Gradient Descent (2000) Llew Mason, Jonathan Baxter, Peter Bartlett, Marcus Frean
• AdaBoost (Wiki)
  • Boosting the margin: A new explanation for the effectiveness of voting methods (1998) Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee

Tree based ensembles

• Random Forests (Wiki, CRAN, PyPI)
  
  • Random Forests (2001) Leo Breiman
  • Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatic (2012) Anne-Laure Boulesteix, Silke Janitza, Jochen Kruppa, Inke R. Konig
  • Variable selection using random forests (2010) Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot
  • Bias in random forest variable importance measures: Illustrations, sources and a solution (2007) Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis and Torsten Hothorn
  • Package: ranger (CRAN)
  • Package: randomForest (CRAN, Paper) (2002) Andy Liaw, Matthew Wiener. Based on work by Leo Breiman and Adele Cutler
  • Generalized Random Forests (Code, CRAN)
    • Generalized Random Forests (2016) Susan Athey, Julie Tibshirani, Stefan Wager
• Gradient Boosting, Stochastic Gradient Boosting (Wiki)
  • Greedy Function Approximation: A Gradient Boosting Machine (1999) Jerome H. Friedman
  • Stochastic Gradient Descent (1999) Jerome H. Friedman
  • XGBoost: A Scalable Tree Boosting System (2016) Tianqi Chen, Carlos Guestrin
  • Out-of-Core GPU Gradient Boosting (2020) Rong Ou
  • Package: XGBoost (Wiki, Code)
  • Package: CatBoost
  • Package: LightGBM

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