The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

mlr3viz

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.

The gallery features a showcase post of the plots in mlr3viz.

Installation

Install the last release from CRAN:

install.packages("mlr3")

Install the development version from GitHub:

remotes::install_github("mlr-org/mlr3viz")

Resources

The gallery features a showcase post of the visualization functions mlr3viz.

Short Demo

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.