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New Advances in High Performance Analytics with R: 'Big Data' Decision Trees and Analysis of Hadoop Data


Sue Ranney, VP Product Development, Revolution Analytics

Classification and regression trees are among the most frequently used algorithms for data analysis and data mining.  The implementation provided in Revolution Analytics’ RevoScaleR package is parallelized, scalable, distributable, and designed with big data in mind. 

Decision trees and all of the other high performance prediction analytics functions provided with RevoScaleR (such as linear and logistic regression, generalized linear models, and k-means clustering) can now also be used to analyze data stored in the HDFS file system.  After specifying the connection parameters to the HDFS file system, some or all of the data can be directly explored, analyzed or quickly and efficiently extracted into a native file system.

 In this webinar we’ll drill down into these two new capabilities and show some examples.

View the YouTube Replay


View slides on Slideshare

Additional Demos:

Predictive Analytics with data in Hadoop using Revolution R Enterprise 6.1 Demonstration