performance: Measure performance of prediction.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

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

1	performance(pred, measures, task = NULL, model = NULL, feats = NULL)

`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.

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

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

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)