rankregr: rankregr
In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"
rankregr R Documentation
rankregr
Description
Feature ranking based on linear least squares regression coefficients
Usage
rankregr(X, Y, lambda = 0.01, nmax = 5)
Arguments
X:
input dataset
Y:
output dataset
nmax:
number of top returned features
Details
rankregr
Feature ranking based on linear least squares regression coefficients
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$feat
ranked.features<-rankregr(X,Y,nmax=3)
gbonte/gbcode documentation built on Aug. 30, 2024, 1:11 a.m.
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| rankregr | R Documentation |
rankregr
Description
Feature ranking based on linear least squares regression coefficients
Usage
rankregr(X, Y, lambda = 0.01, nmax = 5)
Arguments
X: |
input dataset |
Y: |
output dataset |
nmax: |
number of top returned features |
Details
rankregr
Feature ranking based on linear least squares regression coefficients
Value
Indices of nmax top ranked features
Author(s)
Gianluca Bontempi Gianluca.Bontempi@ulb.be
References
Handbook Statistical foundations of machine learning available in https://tinyurl.com/sfmlh
Examples
N<-100
n<-5
neff<-3
R<-regrDataset(N,n,neff,0.1,seed=0)
X<-R$X
Y<-R$Y
real.features<-R$feat
ranked.features<-rankregr(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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