XGBoost : Reliable Large-scale Tree Boosting System

By Tianqi Chen, Carlos Guestrin. 2015

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sabrina Jaeger
2. Samo Turk
3. Simone Fulle
4. Steven Mills
5. Edward Raff
6. Jared Sylvester
7. Luca Oneto
8. Jae Yong Ryu

Citations in the corpus

• Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition (2018, positive): “The XGBoost implementation 30 of GBM was used with 2000 estimators and setting maximum depth of trees to 3 and learning rate to 0.1.”
• Fair Forests: Regularized Tree Induction to Minimize Model Bias (2017, methodology): “XGBoost, a variant of gradient boosting, has been used in the winning solutions to over half of recent Kaggle competitions [8].”