mlr (version 2.19.1)

performance: Measure performance of prediction.

Description

Measures the quality of a prediction w.r.t. some performance measure.

Usage

performance(
  pred,
  measures,
  task = NULL,
  model = NULL,
  feats = NULL,
  simpleaggr = FALSE
)

Value

(named numeric). Performance value(s), named by measure(s).

Arguments

pred

(Prediction)
Prediction object.

measures

(Measure | list of Measure)
Performance measure(s) to evaluate. Default is the default measure for the task, see here getDefaultMeasure.

task

(Task)
Learning task, might be requested by performance measure, usually not needed except for clustering or survival.

model

(WrappedModel)
Model built on training data, might be requested by performance measure, usually not needed except for survival.

feats

(data.frame)
Features of predicted data, usually not needed except for clustering. If the prediction was generated from a task, you can also pass this instead and the features are extracted from it.

simpleaggr

(logical)
If TRUE, aggregation of ResamplePrediction objects is skipped. This is used internally for threshold tuning. Default is FALSE.

See Also

Other performance: ConfusionMatrix, calculateConfusionMatrix(), calculateROCMeasures(), estimateRelativeOverfitting(), makeCostMeasure(), makeCustomResampledMeasure(), makeMeasure(), measures, setAggregation(), setMeasurePars()

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)