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

plotResiduals {mlr} | R Documentation

## Create residual plots for prediction objects or benchmark results.

### Description

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

### Usage

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

### Arguments

`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.

### Value

ggplot2 plot object.

### See Also

Other plot: `plotBMRBoxplots`, `plotBMRRanksAsBarChart`, `plotBMRSummary`, `plotCalibration`, `plotCritDifferences`, `plotLearningCurve`, `plotPartialDependence`, `plotROCCurves`, `plotThreshVsPerf`

[Package mlr version 2.12.1 Index]