View source: R/PredictionRegr.R
autoplot.PredictionRegr | R Documentation |
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.
## S3 method for class 'PredictionRegr'
autoplot(
object,
type = "xy",
binwidth = NULL,
theme = theme_minimal(),
quantile = 1.96,
...
)
object |
(mlr3::PredictionRegr). |
type |
(character(1)): |
binwidth |
( |
theme |
( |
quantile |
( |
... |
(ignored). |
ggplot2::ggplot()
.
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")
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.