https://github.com/cran/FSelector
Revision 970c4e652c26673a06af15db21f6640a9d8d1653 authored by Lars Kotthoff on 30 June 2016, 17:56:47 UTC, committed by cran-robot on 30 June 2016, 17:56:47 UTC
1 parent a6a4107
Tip revision: 970c4e652c26673a06af15db21f6640a9d8d1653 authored by Lars Kotthoff on 30 June 2016, 17:56:47 UTC
version 0.21
version 0.21
Tip revision: 970c4e6
random.forest.importance.Rd
\name{random.forest.importance}
\alias{random.forest.importance}
\title{ RandomForest filter }
\description{
The algorithm finds weights of attributes using RandomForest algorithm.
}
\usage{
random.forest.importance(formula, data, importance.type = 1)
}
\arguments{
\item{formula}{ a symbolic description of a model }
\item{data}{ data to process }
\item{importance.type}{ either 1 or 2, specifying the type of importance measure (1=mean decrease in accuracy, 2=mean decrease in node impurity) }
}
\details{
This is a wrapper for \code{\link[randomForest]{importance}.}
}
\value{
a data.frame containing the worth of attributes in the first column and their names as row names
}
\author{ Piotr Romanski }
\examples{
library(mlbench)
data(HouseVotes84)
weights <- random.forest.importance(Class~., HouseVotes84, importance.type = 1)
print(weights)
subset <- cutoff.k(weights, 5)
f <- as.simple.formula(subset, "Class")
print(f)
}
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