mlr (version 2.6)

plotBenchmarkResult: Create a Trellis-plot for a selected measure.

Description

Plots boxplots for a selected measure across all iterations of the resampling strategy, faceted by the task.id

Usage

plotBenchmarkResult(bmr, measure = NULL, style = "box", order.lrns = NULL,
  order.tsks = NULL, pretty.names = TRUE)

Value

ggplot2 plot object.

References

Manuel J. A. Eugster, Torsten Hothorn and Friedrich Leisch; Domain-Based Benchmark Experiments: Exploratory and Inferential Analysis, AUSTRIAN JOURNAL OF STATISTICS Volume 41 (2012), Number 1, 5–26, but does not include any clustering or sorting.

See Also

Other benchmark: BenchmarkResult, benchmark, convertBMRToRankMatrix, friedmanPostHocTestBMR, friedmanTestBMR, generateBenchmarkSummaryData, generateCritDifferencesData, generateRankMatrixAsBarData, getBMRAggrPerformances, getBMRFeatSelResults, getBMRFilteredFeatures, getBMRLearnerIds, getBMRLearners, getBMRMeasureIds, getBMRMeasures, getBMRPerformances, getBMRPredictions, getBMRTaskIds, getBMRTuneResults, plotBenchmarkSummary, plotCritDifferences, plotRankMatrixAsBar

Other plot: plotBenchmarkSummary, plotCalibration, plotCritDifferences, plotFilterValuesGGVIS, plotFilterValues, plotLearningCurveGGVIS, plotLearningCurve, plotPartialPredictionGGVIS, plotPartialPrediction, plotROCCurves, plotROCRCurvesGGVIS, plotROCRCurves, plotRankMatrixAsBar, plotThreshVsPerfGGVIS, plotThreshVsPerf

Examples

lrns = list(makeLearner("classif.nnet"), makeLearner("classif.rpart"))
tasks = list(iris.task, sonar.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, mmce, ber, featperc)
res = benchmark(lrns, tasks, rdesc, meas)
plotBenchmarkResult(res, acc)