plotROCCurves: Plots a ROC curve using ggplot2.

In riebetob/mlr: Machine Learning in R

Description

Plots a ROC curve from predictions.

Usage

plotROCCurves(obj, measures, diagonal = TRUE, pretty.names = TRUE,
  facet.learner = FALSE)

Arguments

obj	[ThreshVsPerfData] Result of generateThreshVsPerfData.
measures	[list(2) of Measure] Default is the first 2 measures passed to generateThreshVsPerfData.
diagonal	[logical(1)] Whether to plot a dashed diagonal line. Default is TRUE.
pretty.names	[logical(1)] Whether to use the Measure name instead of the id in the plot. Default is TRUE.
facet.learner	[logical(1)] Weather to use facetting or different colors to compare multiple learners. Default is FALSE.

Value

ggplot2 plot object.

See Also

Other plot: plotBMRBoxplots, plotBMRRanksAsBarChart, plotBMRSummary, plotCalibration, plotCritDifferences, plotFilterValuesGGVIS, plotLearningCurveGGVIS, plotLearningCurve, plotPartialDependenceGGVIS, plotPartialDependence, plotResiduals, plotThreshVsPerfGGVIS, plotThreshVsPerf

Other thresh_vs_perf: generateThreshVsPerfData, plotThreshVsPerfGGVIS, plotThreshVsPerf

Examples

lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
roc = generateThreshVsPerfData(pred, list(fpr, tpr))
plotROCCurves(roc)

r = bootstrapB632plus(lrn, sonar.task, iters = 3)
roc_r = generateThreshVsPerfData(r, list(fpr, tpr), aggregate = FALSE)
plotROCCurves(roc_r)

r2 = crossval(lrn, sonar.task, iters = 3)
roc_l = generateThreshVsPerfData(list(boot = r, cv = r2), list(fpr, tpr), aggregate = FALSE)
plotROCCurves(roc_l)

riebetob/mlr documentation built on May 20, 2019, 5:58 p.m.