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 viageom_smooth(method = "lm")
to visualize the trend between x and y (by default colored blue).In addition
geom_abline()
withslope = 1
is added to the plot.Note that
geom_smooth()
andgeom_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 respectivegeom
.
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'