Package website: release | dev
mlr3viz is the visualization package of the
mlr3 ecosystem. It features plots for mlr3
objects such as tasks, learners, predictions, benchmark results, tuning
instances and filters via the autoplot() generic of
ggplot2. The package draws plots with
the viridis color
palette and applies the minimal
theme.
Visualizations include barplots, boxplots, histograms, ROC curves, and
Precision-Recall curves.
Install the last release from CRAN:
install.packages("mlr3")Install the development version from GitHub:
remotes::install_github("mlr-org/mlr3viz")The gallery features a showcase post of the visualization functions mlr3viz.
library(mlr3)
library(mlr3viz)
task = tsk("pima")
learner = lrn("classif.rpart", predict_type = "prob")
rr = resample(task, learner, rsmp("cv", folds = 3), store_models = TRUE)
# Default plot for task
autoplot(task, type = "target")
# ROC curve for resample result
autoplot(rr, type = "roc")
For more example plots you can have a look at the pkgdown references of the respective functions.