New Advances in High Performance Analytics with R: 'Big Data' Decision Trees and Analysis of Hadoop Data
|Presented:||Thursday, November 15, 2012|
|Presenter:||Sue Ranney, VP Product Development
Revolution R Enterprise 6.1 includes two important advances in high performance predictive analytics with R: (1) big data decision trees, and (2) the ability to easily extract and perform predictive analytics on data stored in the Hadoop Distributed File System (HDFS).
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
Additional Demos:Predictive Analytics with data in Hadoop using Revolution R Enterprise 6.1 Demonstration
About the Speaker
Sue Ranney oversees Revolution Analytics' product planning and execution throughout the product lifecycle. She was co-founder of ExaMetrix, the producer of the ExaStat open source environment for analyzing huge data sets. Ranney also served as vice president of development for the data analysis products division at MathSoft (now TIBCO Software), where she co-managed S-Plus releases, and was the co-founder of TriMetrix —the creators of the Axum technical graphics and data analysis package. She holds a Ph.D. from the University of Wisconsin.