Big-data Naive Bayes and Classification Trees with R and Netezza

March 8, 2012

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

The IBM Netezza analytics appliances combine high-capacity storage for Big Data with a massively-parallel processing platform for high-performance computing. With the addition of Revolution R Enterprise for IBM Netezza, you can use the power of the R language to build predictive models on Big Data.

In the demonstration below, Revolution Analytics' Derek Norton analyzes loan approval data stored on the IBM appliance. You'll see the R code used to:

  • Explore the raw data (with summary statistics and charts)
  • Prepare the data for statistical analysis, and create training and test sets
  • Create predictive models using classificiation trees and Naïve Bayes
  • Predict using the models, and evaluate model performance using confusion matrices


Note that while R code is being run on Derek's laptop, the raw data is never moved from the appliance, and the analytic computations take place "in-database" within the appliance itself (where the Revolution R Enterprise engine is also running on each parallel core). 

This demo was included in the recent webinar, Turbo-Charge Your Analytics with IBM Netezza for which you can find slides and a replay at the link below.

Revolution Anlaytics Webinars: Turbo-Charge Your Analytics with IBM Netezza and Revolution R Enterprise: A Step-by-Step Approach for Acceleration and Innovation

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