Weka goes BIG

December 4, 2013

funny_science_nerd_cartoon_character_custom_flyer-rb4a8aff0894a4e25932056b8852f8b18_vgvyf_8byvr_512.jpgThe beakers are bubbling more violently than usual at Pentaho Labs and this time predictive analytics is the focus.  The lab coat, pocket-protector and taped glasses clad scientists have turned their attention to the Weka machine learning software.

Weka, a collection of machine learning algorithms for predictive analytics and data mining, has a number of useful applications. Examples include, scoring credit risk, predicting downtime of machines and analyzing sentiment in social feeds.  The technology can be used to facilitate automatic knowledge discovery by uncovering hidden patterns in complex datasets, or to develop accurate predictive models for forecasting.

Organizations have been building predictive models to aid decision making for a number of years, but the recent explosion in the volume of data being recorded (aka “Big Data”) provides unique challenges for data mining practitioners. Weka is efficient and fast when running against datasets that fit in main memory, but larger datasets often require sampling before processing. Sampling can be an effective mechanism when samples are representative of the underlying problem, but in some cases the loss of information can negatively impact predictive performance.

To combat information loss, and scale Weka’s wide selection of predictive algorithms to large data sets, the folks at Pentaho Labs developed a framework to run Weka in Hadoop. Now the sort of tasks commonly performed during the development of a predictive solution – such as model construction, tuning, evaluation and scoring – can be carried out on large datasets without resorting to down-sampling the data. Hadoop was targeted as the initial distributed platform for the system, but the Weka framework contains generic map-reduce building blocks that can be used to develop similar functionality in other distributed environments.

If you’re a predictive solution developer or a data scientist, the new Weka framework is a much faster path to solution development and deployment.  Just think of the questions you can ask at scale!

To learn more technical details about the Weka Hadoop framework I suggest to read the blog, Weka and Hadoop Part 1, by Mark Hall, Weka core developer at Pentaho.

Also, check out Pentaho Labs to learn more about Predictive Analytics from Pentaho, and to see some of the other cool things the team has brewing.

Chuck Yarbrough
Technical Solutions Marketing


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