plotLearningCurve: Plot learning curve data using ggplot2.

In riebetob/mlr: Machine Learning in R

Description

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 class LearningCurveData.
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”.
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.

Value

ggplot2 plot object.

See Also

Other learning_curve: generateLearningCurveData, plotLearningCurveGGVIS

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

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