plotThreshVsPerfGGVIS: Plot threshold vs. performance(s) for 2-class classification...
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

Plots threshold vs. performance(s) data that has been generated with generateThreshVsPerfData.

1 2	plotThreshVsPerfGGVIS(obj, interaction = "measure", mark.th = NA_real_, pretty.names = TRUE)

`obj`	[`ThreshVsPerfData`] Result of `generateThreshVsPerfData`.
`interaction`	[`character(1)`] Selects “measure” or “learner” to be used in a Shiny application making the `interaction` variable selectable via a drop-down menu. This variable must have more than one unique value, otherwise it will be ignored. The variable not chosen is mapped to color if it has more than one unique value. Note that if there are multiple learners and multiple measures interactivity is necessary as ggvis does not currently support facetting or subplots. The default is “measure”.
`mark.th`	[`numeric(1)`] Mark given threshold with vertical line? Default is `NA` which means not to do it.
`pretty.names`	[`logical(1)`] Whether to use the `Measure` name instead of the id in the plot. Default is `TRUE`.

a ggvis plot object.

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

Other thresh_vs_perf: generateThreshVsPerfData, plotROCCurves, plotThreshVsPerf

## Not run: 
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, sonar.task)
pred = predict(mod, sonar.task)
pvs = generateThreshVsPerfData(pred, list(tpr, fpr))
plotThreshVsPerfGGVIS(pvs)

## End(Not run)