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Random Jungle

Random Jungle is an implementation of Random Forests™, used to analyze high dimensional data. In genetics, the algorithm can be used for analyzing big Genome Wide Association (GWA) data. Per the authors, some of the most interesting features of the algorithm include variable selection, missing value imputation, classifier creation, generalization error estimation and sample proximities between pairs of cases. Some of the commonly-used measurements implemented include Gini importance, permutation importance, conditional importance measures, and variable backward elimination. When multiple CPUs are available, Random Jungle uses multithreading and Message Passing Interface (MPI) parallellization.

Random Jungle Sparse uses less memory than Random Jungle, but data should only use the values of 0, 1, 2, and 3 for missing code.

Usage

Random Jungle and Random Jungle Sparse have a large number of command line parameters; see Section 3.1 of the program documentation, RandomJungleManual.pdf.

Example Data

Test data can be found at:

Community Data ? iplantcollaborative ? example_data ? randomjungle ? input

Results using the parameter file including with the relevant input files can be found at:

Community Data ? iplantcollaborative ? example_data ? randomjungle ? output

Command-line options to generate the output files are found in run.log and run2.log which are found among the other output files.

Tool Source

Binary	; ; All versions;
Source	All versions
Version	1.3.0
User Guide	and RandomJunglePaper.pdf
Requires	N/A
JSON	and

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Random Jungle 2 (archived)