mrmr: mrmr
In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"
mrmr R Documentation
mrmr
Description
mRMR filter based on mutual information
Usage
mrmr(X, Y, nmax = 5, back = FALSE)
Arguments
X:
input dataset
Y:
output dataset
nmax:
number of top returned features
back:
if TRUE, backward reordering based on linear regression
Details
mrmr
mRMR (minimum redundancy maximum relevance) filter based on mutual information
Value
Indices of nmax top ranked features
Author(s)
Gianluca Bontempi Gianluca.Bontempi@ulb.be
References
Handbook Statistical foundations of machine learning available in https://tinyurl.com/sfmlh
Examples
N<-100
n<-5
neff<-3
R<-regrDataset(N,n,neff,0.1,seed=0)
X<-R$X
Y<-R$Y
real.features<-R$feat
ranked.features<-mrmr(X,Y,nmax=3)
gbonte/gbcode documentation built on Feb. 27, 2024, 7:38 a.m.
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| mrmr | R Documentation |
mrmr
Description
mRMR filter based on mutual information
Usage
mrmr(X, Y, nmax = 5, back = FALSE)
Arguments
X: |
input dataset |
Y: |
output dataset |
nmax: |
number of top returned features |
back: |
if TRUE, backward reordering based on linear regression |
Details
mrmr
mRMR (minimum redundancy maximum relevance) filter based on mutual information
Value
Indices of nmax top ranked features
Author(s)
Gianluca Bontempi Gianluca.Bontempi@ulb.be
References
Handbook Statistical foundations of machine learning available in https://tinyurl.com/sfmlh
Examples
N<-100
n<-5
neff<-3
R<-regrDataset(N,n,neff,0.1,seed=0)
X<-R$X
Y<-R$Y
real.features<-R$feat
ranked.features<-mrmr(X,Y,nmax=3)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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