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
- 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

```
if (requireNamespace("mlr3")) {
library(mlr3)
library(mlr3viz)
task = tsk("boston_housing")
learner = lrn("regr.rpart")
object = learner$train(task)$predict(task)
head(fortify(object))
autoplot(object)
autoplot(object, type = "histogram", binwidth = 1)
autoplot(object, type = "residual")
}
#> `geom_smooth()` using formula 'y ~ x'
```