plotResiduals: Create residual plots for prediction objects or benchmark...
In Najah-lshanableh/R-data-mining2: Machine Learning in R

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

1 2	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.