View source: R/PredictionClassif.R
autoplot.PredictionClassif | R Documentation |
Generates plots for mlr3::PredictionClassif, depending on argument type
:
"stacked"
(default): Stacked barplot of true and estimated class labels.
"roc"
: ROC curve (1 - specificity on x, sensitivity on y).
Requires package precrec.
"prc"
: Precision recall curve.
Requires package precrec.
"threshold"
: Systematically varies the threshold of the mlr3::PredictionClassif
object and plots the resulting performance as returned by measure
.
## S3 method for class 'PredictionClassif' autoplot(object, type = "stacked", measure = NULL, ...)
object |
(mlr3::PredictionClassif). |
type |
(character(1)): |
measure |
(mlr3::Measure) |
... |
( |
ggplot2::ggplot()
object.
The theme_mlr3()
and viridis color maps are applied by default to all
autoplot()
methods. To change this behavior set
options(mlr3.theme = FALSE)
.
Saito T, Rehmsmeier M (2017). “Precrec: fast and accurate precision-recall and ROC curve calculations in R.” Bioinformatics, 33(1), 145-147. doi: 10.1093/bioinformatics/btw570.
library(mlr3) library(mlr3viz) task = tsk("spam") learner = lrn("classif.rpart", predict_type = "prob") object = learner$train(task)$predict(task) head(fortify(object)) autoplot(object) autoplot(object, type = "roc") autoplot(object, type = "prc")
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