Planet: Massively parallel learning of tree ensembles with mapreduce. Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google’s computing infrastructure is based on commodity hardware. In this paper, we describe PLANET: a scalable distributed framework for learning tree models over large datasets. PLANET defines tree learning as a series of distributed computations, and implements each one using the MapReduce model of distributed computation. We show how this framework supports scalable construction of classification and regression trees, as well as ensembles of such models. We discuss the benefits and challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach by applying it to a real world learning task from the domain of computational advertising.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
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- Wang, Ran; He, Yu-Lin; Chow, Chi-Yin; Ou, Fang-Fang; Zhang, Jian: Learning ELM-tree from big data based on uncertainty reduction (2015)
- Nasridinov, Aziz; Lee, Yangsun; Park, Young-Ho: Decision tree construction on GPU: ubiquitous parallel computing approach (2014) ioport
- Hong, Chun-Tao; Chen, De-Hao; Chen, Yu-Bei; Chen, Wen-Guang; Zheng, Wei-Min; Lin, Hai-Bo: Providing source code level portability between CPU and GPU with MapCG (2012) ioport