##### https://github.com/cran/FSelector

Tip revision:

**a6a4107a08051dfddc3c733102d002fd8617ab9e**authored by**Lars Kotthoff**on**25 October 2014, 00:00 UTC****version 0.20** Tip revision:

**a6a4107** oneR.Rd

```
\name{oneR}
\alias{oneR}
\title{ OneR algorithm }
\description{
The algorithms find weights of discrete attributes basing on very simple association rules involving only one attribute in condition part.
}
\usage{
oneR(formula, data)
}
\arguments{
\item{formula}{ a symbolic description of a model }
\item{data}{ data to process }
}
\details{
The algorithm uses OneR classifier to find out the attributes' weights. For each attribute it creates a simple rule based only on that attribute and then calculates its error rate.
}
\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 <- oneR(Class~., HouseVotes84)
print(weights)
subset <- cutoff.k(weights, 5)
f <- as.simple.formula(subset, "Class")
print(f)
}
```