Menu

Tree [r1] /
 History

HTTPS access


File Date Author Commit
 doc 2010-09-01 banoslo [r1]
 pkgskeleton 2010-09-01 banoslo [r1]
 src 2010-09-01 banoslo [r1]
 .cproject 2010-09-01 banoslo [r1]
 .project 2010-09-01 banoslo [r1]
 README 2010-09-01 banoslo [r1]

Read Me

THE BASIC Random Jungle (RJ) README
See "INSTALL" for help on installation


1. INTRODUCTION

This directory contains the source code, tests, autobuild files and autobuild
tools for RJ. RJ is a generalized implementation of Random Forests(tm) (RF) by
Leo Breiman and Adele Cutler. In genetics, it is an well established method for
analysing genetic data (i.e. GWA data).

RJ is free software distributed under a GNU-style copyleft.

RF is a powerful machine learning method. Most interesting features [1]:
- Variable selection: Estimates the importance of variables
- Efficiency:         It runs efficiently on large data.
- Missing values:     It has an effective method for estimating missing data.
- Classifier:         It creates classifier for further analyses.
- General. err.:      It generates an internal unbiased estimate of the
                      generalization error as the building progresses.
- Proximities:        It computes proximities between pairs of cases that can be
                      used in clustering, locating outliers, or (by scaling)
                      give interesting views of the data. Can be used as the
                      distance matrix for Multidimensional Scaling (MDS)


2. HISTORY

RJ was initially written by Daniel F. Schwarz of the Institute of Medical
Biometry and Statistics of University of Lubeck in Germany.
He started in the beginning of 2008.


3. GOALS

Create a machine learning tool for analysing data as an equivalent to
alternative statistical methods.

4. DEPS
UBUNTU (packages):
- gcc
- g++
- gfortran
- libtool
- autoconf
- libboost-dev
- zlib1g-dev
- libgsl0-dev
- libxml2-dev

Linker libraries:
- xml2
- gsl
- gslcblas
- z 

MPI:
- mpi-default-dev
- mpi-default-bin

Make documentation:
- texinfo
- texlive


Sincerely,
Daniel


[1] http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
MongoDB Logo MongoDB