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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/performance.R
\name{performance}
\alias{performance}
\title{Measure performance of prediction.}
\usage{
performance(
pred,
measures,
task = NULL,
model = NULL,
feats = NULL,
simpleaggr = FALSE
)
}
\arguments{
\item{pred}{(\link{Prediction})\cr
Prediction object.}
\item{measures}{(\link{Measure} | list of \link{Measure})\cr
Performance measure(s) to evaluate.
Default is the default measure for the task, see here \link{getDefaultMeasure}.}
\item{task}{(\link{Task})\cr
Learning task, might be requested by performance measure, usually not needed except for clustering or survival.}
\item{model}{(\link{WrappedModel})\cr
Model built on training data, might be requested by performance measure, usually not needed except for survival.}
\item{feats}{(\link{data.frame})\cr
Features of predicted data, usually not needed except for clustering.
If the prediction was generated from a \code{task}, you can also pass this instead and the features
are extracted from it.}
\item{simpleaggr}{(\link{logical})\cr
If TRUE, aggregation of \code{ResamplePrediction} objects is skipped. This is used internally for threshold tuning. Default is \code{FALSE}.}
}
\value{
(named \link{numeric}). Performance value(s), named by measure(s).
}
\description{
Measures the quality of a prediction w.r.t. some performance measure.
}
\examples{
training.set = seq(1, nrow(iris), by = 2)
test.set = seq(2, nrow(iris), by = 2)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda")
mod = train(lrn, task, subset = training.set)
pred = predict(mod, newdata = iris[test.set, ])
performance(pred, measures = mmce)
# Compute multiple performance measures at once
ms = list("mmce" = mmce, "acc" = acc, "timetrain" = timetrain)
performance(pred, measures = ms, task, mod)
}
\seealso{
Other performance:
\code{\link{ConfusionMatrix}},
\code{\link{calculateConfusionMatrix}()},
\code{\link{calculateROCMeasures}()},
\code{\link{estimateRelativeOverfitting}()},
\code{\link{makeCostMeasure}()},
\code{\link{makeCustomResampledMeasure}()},
\code{\link{makeMeasure}()},
\code{\link{measures}},
\code{\link{setAggregation}()},
\code{\link{setMeasurePars}()}
}
\concept{performance}
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