Visualizations for mlr3::PredictionRegr.
The argument `type`

controls what kind of plot is drawn.
Possible choices are:

`"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).`"confidence`

: Scatterplot of "true" response vs. "predicted" response with confidence intervals. Error bars calculated as object$reponse +- quantile * object$se and so only possible with`predict_type = "se"`

.`geom_abline()`

with`slope = 1`

is added to the plot.

## Usage

```
# S3 method for PredictionRegr
autoplot(
object,
type = "xy",
binwidth = NULL,
theme = theme_minimal(),
quantile = 1.96,
...
)
```

## Arguments

- object
- type
(character(1)):

Type of the plot. See description.- binwidth
(

`integer(1)`

)

Width of the bins for the histogram.- theme
(

`ggplot2::theme()`

)

The`ggplot2::theme_minimal()`

is applied by default to all plots.- quantile
(

`numeric(1)`

)

Quantile multiplier for standard errors for`type="confidence"`

. Default 1.96.- ...
(ignored).

## 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")
if (requireNamespace("mlr3learners")) {
library(mlr3learners)
learner = lrn("regr.ranger", predict_type = "se")
object = learner$train(task)$predict(task)
autoplot(object, type = "confidence")
}
}
```