Description:

Big Random Forests: Classification and Regression Forests for Large Data Sets: This is an implementation of Leo Breiman's and Adele Cutler's Random Forest algorithms for classification and regression, with optimizations for performance and for handling of data sets that are too large to be processed in memory. Forests can be built in parallel at two levels. First, trees can be grown in parallel on a single machine using foreach. Second, multiple forests can be built in parallel on multiple machines, then merged into one. For large data sets, disk-based big.matrix's may be used for storing data and intermediate computations, to prevent excessive virtual memory swapping by the operating system. Currently, only classification forests with a subset of the functionality in Breiman and Cutler's original code are implemented. More functionality and regression trees may be added in the future.

Changes to previous version:

Fetched by r-cran-robot on 2015-11-01 00:00:04.072762

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Tags: R-Cran

Archive: download here

Other available revisons

Version	Changelog	Date
0.1-11	Fetched by r-cran-robot on 2015-11-01 00:00:04.072762	June 1, 2014, 00:00:03
0.1-6	Fetched by r-cran-robot on 2014-05-01 00:00:04.777561	July 1, 2013, 00:00:03
0.1-5	Fetched by r-cran-robot on 2013-06-01 00:00:04.895100	May 1, 2013, 00:00:04
0.1-4	Fetched by r-cran-robot on 2013-04-01 00:00:03.917296	April 1, 2013, 00:00:03
0.1-3	Initial Announcement on mloss.org by r-cran-robot	March 1, 2013, 00:00:03

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