Visualizes data size (percentage used for model) vs. performance measure(s).
Usage
plotLearningCurve(
obj,
facet = "measure",
pretty.names = TRUE,
facet.wrap.nrow = NULL,
facet.wrap.ncol = NULL
)
Arguments
- obj
(LearningCurveData)
Result of generateLearningCurveData, with classLearningCurveData
.- facet
(
character(1)
)
Selects “measure” or “learner” to be the facetting variable. The variable mapped tofacet
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”.- pretty.names
(
logical(1)
)
Whether to use the Measure name instead of the id in the plot. Default isTRUE
.- facet.wrap.nrow, facet.wrap.ncol
(integer)
Number of rows and columns for facetting. Default for both isNULL
. In this case ggplot'sfacet_wrap
will choose the layout itself.
See also
Other learning_curve:
generateLearningCurveData()
Other plot:
createSpatialResamplingPlots()
,
plotBMRBoxplots()
,
plotBMRRanksAsBarChart()
,
plotBMRSummary()
,
plotCalibration()
,
plotCritDifferences()
,
plotPartialDependence()
,
plotROCCurves()
,
plotResiduals()
,
plotThreshVsPerf()