Living in the age of big data we ask what to do when we have the good fortune to be presented with a huge amount of supervised training data? Most often at large scale we are presented with the un-supervised problems of characterization and information extraction; but some problem domains offer an almost limitless supply of supervised training data (such as using older data to build models that predict the near future). Having too much training data is a good problem to have and there are ways to use traditional methods (like logistic regression) at this scale. We present an “out of core” logistic regression implementation and a quick example in Apache Hadoop running on Amazon Elastic MapReduce. This presentation assumes familiarity with Unix style command lines, Java and Hadoop. Read more…

Categories: math programming, Programming, Statistics Tags: Amazon Elastic MapReduce, Big Data, EC2, Hadoop, Logistic Regression, Machine Learning, Mahout, Map Reduce, S3
We have been living in the age of “big data” for some time now. This is an age where incredible things can be accomplished through the effective application of statistics and machine learning at large scale (for example see: “The Unreasonable Effectiveness of Data” Alon Halevy, Peter Norvig, Fernando Pereira, IEEE Intelligent Systems (2009)). But I have gotten to thinking about the period before this. The period before we had easy access to so much data, before most computation was aggregation and before we accepted numerical analysis style convergence as “efficient.” A small problem I needed to solve (as part of a bigger project) reminded me what theoretical computer scientists did then: we worried about provable worst case efficiency.

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Categories: Computer Science, Expository Writing, Mathematics Tags: Age of Big Data, Big Data, Mathematical Bedside Reading, Mean, Mean of Medians, Median, Median of Means, Theorist, Winsorized mean