mlr: performance – R documentation

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mlr

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
)

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`.

Value

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

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)

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release