R: Create residual plots for prediction objects or benchmark...

plotResiduals {mlr}

R Documentation

Create residual plots for prediction objects or benchmark results.

Plots for model diagnostics. Provides scatterplots of true vs. predicted values and histograms of the model's residuals.

plotResiduals(obj, type = "scatterplot", loess.smooth = TRUE,
  rug = TRUE, pretty.names = TRUE)

`obj`	(Prediction \| BenchmarkResult) Input data.
`type`	Type of plot. Can be “scatterplot”, the default. Or “hist”, for a histogram, or in case of classification problems a barplot, displaying the residuals.
`loess.smooth`	(`logical(1)`) Should a loess smoother be added to the plot? Defaults to `TRUE`. Only applicable for regression tasks and if `type` is set to `scatterplot`.
`rug`	(`logical(1)`) Should marginal distributions be added to the plot? Defaults to `TRUE`. Only applicable for regression tasks and if `type` is set to `scatterplot`.
`pretty.names`	(`logical(1)`) Whether to use the short name of the learner instead of its ID in labels. Defaults to `TRUE`. Only applicable if a BenchmarkResult is passed to `obj` in the function call, ignored otherwise.

ggplot2 plot object.