mimr-open-paren-minimum-interaction-maximum-relevance-close-paren: mimr

In gbonte/gbcode: Code from the handbook "Statistical foundations of machine learning"

mimr

Description

mimr filter based on mutual information

Usage

mimr(
  X,
  Y,
  nmax = 5,
  first = NULL,
  init = FALSE,
  lambda = 0.5,
  fast.inter = 0,
  back = FALSE,
  spouse.removal = TRUE,
  caus = 1
)

Arguments

X:	input dataset
Y:	output dataset
nmax:	number of top returned features
back:	if TRUE, backward reordering based on linear regression
caus:	if caus =1 it searches for causes otherwise if caus=-1 it searches for effects

Details

mimr

mimr (minimum interaction maximum relevance) filter based on mutual information

Value

Indices of nmax top ranked features

Author(s)

Gianluca Bontempi [e-mail]

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<-mimr(X,Y,nmax=3)

gbonte/gbcode documentation built on Aug. 30, 2024, 1:11 a.m.