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).

# 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.

Examples

library(mlr3) library(mlr3viz) task = tsk("boston_housing") learner = lrn("regr.rpart") object = learner$train(task)$predict(task) head(fortify(object))
#> row_id 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'