autoplot.PredictionClassif: Plot for PredictionClassif

In mlr3viz: Visualizations for 'mlr3'

Plot for PredictionClassif

Description

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.

Usage

## S3 method for class 'PredictionClassif'
autoplot(object, type = "stacked", measure = NULL, ...)

Arguments

object	(mlr3::PredictionClassif).
type	(character(1)): Type of the plot. See description.
measure	(mlr3::Measure) Performance measure to use.
...	(any): Additional arguments, passed down to the respective geom or plotting function.

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).

References

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.

Examples

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")

mlr3viz documentation built on May 25, 2022, 5:06 p.m.