Scaling Out Random Forest

Denis C. Bauer, et al, explain VariantSpark RF, a random forest algorithm designed for huge numbers of variables:

VariantSpark RF starts by randomly assigning subsets of the data to Spark Executors for decision tree building (Fig 1). It then calculates the best split over all nodes and trees simultaneously. This implementation avoids communication bottlenecks between Spark Driver and Executors as information exchange is minimal, allowing it to build large numbers of trees efficiently. This surveys the solution space appropriately to cater for millions of features and thousands of samples.

Furthermore, VariantSpark RF has memory efficient representation of genomics data, optimized communication patterns and computation batching. It also provides efficient implementation of Out-Of-Bag (OOB) error, which substantially simplifies parameter tuning over the computationally more costly alternative of cross-validation.

We implemented VariantSpark RF in scala as it is the most performant interface languages to Apache Spark. Also, new updates to Spark and the interacting APIs will be deployed in scala first, which has been important when working on top of a fast evolving framework.

Give it a read.  Thankfully, I exhibit few of the traits of the degenerative disease known as Hipsterism.

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