plotThreshVsPerf: 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.

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plotThreshVsPerf(obj, measures = obj$measures, facet = "measure",
  mark.th = NA_real_, pretty.names = TRUE, facet.wrap.nrow = NULL,
  facet.wrap.ncol = NULL)

`obj`	[`ThreshVsPerfData`] Result of `generateThreshVsPerfData`.
`measures`	[`Measure` \| list of `Measure`] Performance measure(s) to plot. Must be a subset of those used in `generateThreshVsPerfData`. Default is all the measures stored in `obj` generated by `generateThreshVsPerfData`.
`facet`	[`character(1)`] Selects “measure” or “learner” to be the facetting variable. The variable mapped to `facet` 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. 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`.
`facet.wrap.nrow, facet.wrap.ncol`	[`integer()`] Number of rows and columns for facetting. Default for both is `NULL`. In this case ggplot's `facet_wrap` will choose the layout itself.

ggplot2 plot object.

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

Other thresh_vs_perf: generateThreshVsPerfData, plotROCCurves, plotThreshVsPerfGGVIS

lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, sonar.task)
pred = predict(mod, sonar.task)
pvs = generateThreshVsPerfData(pred, list(acc, setAggregation(acc, train.mean)))
plotThreshVsPerf(pvs)