mlr (version 2.13)

plotROCCurves: Plots a ROC curve using ggplot2.

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: createSpatialResamplingPlots, plotBMRBoxplots, plotBMRRanksAsBarChart, plotBMRSummary, plotCalibration, plotCritDifferences, plotLearningCurve, plotPartialDependence, plotResiduals, plotThreshVsPerf

Other thresh_vs_perf: generateThreshVsPerfData, plotThreshVsPerf

Examples

# NOT RUN {
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)
# }