This function computes the proximity matrix by Random Forest algorithm. Proximity values ranges from 0 (least similar) to 1 (perfect match).
1 2 
train 
An object of class 
train.label 
A vector of actual class labels (0 or 1) of the training set. Should be numeric not factor. 
test 
An object of class 
N 
Number of repetition for calculating the proximity matrix, final proximity matrix is average of these repeats. We recommend to set a large number, so that stable proximity matrix will be produced. Default is 50. 
Parallel 
Should proximity calculation use the parallel processing procedure? Default is FALSE. 
ncpus 
Number of acores assign to the parallel computation. Default is 2. 
A list object with following components:
prox.train 
A square symmetric matrix contains the proximity values of the training set . 
prox.test 
A rectangular square matrix contains the proximity values between test set (rows) and training set (columns). Only returned when test set is supplied. 
Askar Obulkasim
Maintainer: Askar Obulkasim <askar703@gmail.com>
Breiman, L. (2001), Random Forest, 45, 532.
1 2 3 4 5 6 7 8 9 10  data(CNS)
train < t(CNS$cli[1:40,])
test < t(CNS$cli[41:60,])
train.label < CNS$class[1:40]
##without parallel processing procedure
Prox < Proximity(train, train.label, test, N = 2)
##with parallel processing procedure
## Not run: Prox < Proximity(train, train.label, test,
N = 50, Parallel = TRUE, ncpus = 10)
## End(Not run)

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