MapReduce and Its Application to Massively Parallel Learning of Decision Tree Ensembles
Biswanath Panda, Joshua S. Herbach, Sugato Basu, Roberto J. Bayardo · Cambridge University Press eBooks · 2011
In this chapter we look at leveraging the MapReduce distributed computing framework (Dean and Ghemawat, 2004) for parallelizing machine learning methods of wide interest, with a specific focus on learning ensembles of classification or regression trees. Building a production-ready implementation of a distributed learning algorithm can be a complex task. With the wide and growing availability of MapReduce-capable computing infrastructures, it is natural to ask whether such infrastructures may be of use in parallelizing common data mining tasks such as tree learning. For many data mining applications, MapReduce may offer scalability as well as ease of deployment in a production setting (for reasons explained later). We initially give an overview of MapReduce and outline its application in a classic clustering algorithm, k -means. Subsequently, we focus on 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. 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. MapReduce is a simple model for distributed computing that abstracts away many of the difficulties in parallelizing data management operations across a cluster of commodity machines.