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Xgboost: A scalable tree boosting system

Users: 3 - Average Rating: 4.33

In this paper, tree boosting algorithm is described, which is a very popular and effective solution widely recognized in several machine learning and data mining competitions. The main reason for the success of XGBoost is its scalability in different scenarios: it runs 10 times faster than the existing popular solutions and scales beyond billions of examples using far fewer resources than existing systems. In particular, the authors introduce a novel sparsity-aware algorithm for handling sparse data and weighted quantile sketch for approximate tree learning which both contribute to the scalability of XGBoost; more importantly, they provide insights on cache access patterns, and out-of-core computations (data compression and sharding) to build a scalable tree boosting system.
Different datasets have been considered to evaluate the scaling property of the system and the impact of out-of-core computations and sparsity-aware algorithm.

Scientific Paper

Data Analytics, Machine Learning

Target Group:


Cite as:
Chen, T. and Guestrin, C., Xgboost: A scalable tree boosting system, Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (2016): 785-794.

Author of the review:
Giulia Cademartori
University of Genoa


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Andrej KoŇ°ir

A very flexible classifier applicable to non-linear spaces with relatively simple configuration parameters. If the classification space is complex and poorly understood, is a good way to start with.

Mauro Bozzetti

Great Resource!!

Joana Lopes

Very Useful