Plot for PredictionRegr

Generates plots for mlr3::PredictionRegr, depending on argument type:

• "xy" (default): Scatterplot of "true" response vs. "predicted" response. By default a linear model is fitted via geom_smooth(method = "lm") to visualize the trend between x and y (by default colored blue).

  • In addition geom_abline() with slope = 1 is added to the plot.

  • Note that geom_smooth() and geom_abline() may overlap, depending on the given data.

• "histogram": Histogram of residuals: \(r = y - \hat{y}\).

• "residual": Plot of the residuals, with the response \(\hat{y}\) on the "x" and the residuals on the "y" axis.

  • By default a linear model is fitted via geom_smooth(method = "lm") to visualize the trend between x and y (by default colored blue).

Usage

# S3 method for PredictionRegr
autoplot(object, type = "xy", ...)

Arguments

object

(mlr3::PredictionRegr).

type

(character(1)):
Type of the plot. See description.

...

(any): Additional arguments, passed down to the respective geom.

Value

ggplot2::ggplot() object.

Theme

The theme_mlr3() and viridis color maps are applied by default to all autoplot() methods. To change this behavior set options(mlr3.theme = FALSE).

Examples

library(mlr3)
library(mlr3viz)

task = tsk("boston_housing")
learner = lrn("regr.rpart")
object = learner$train(task)$predict(task)

head(fortify(object))
#>    row_ids truth response
#> 1:       1  24.0 23.72519
#> 2:       2  21.6 19.55954
#> 3:       3  34.7 35.10313
#> 4:       4  33.4 35.10313
#> 5:       5  36.2 35.10313
#> 6:       6  28.7 29.98333
autoplot(object)
#> `geom_smooth()` using formula 'y ~ x'

autoplot(object, type = "histogram", binwidth = 1)

autoplot(object, type = "residual")
#> `geom_smooth()` using formula 'y ~ x'