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mlr3summary

Concise, informative summaries of machine learning models. Based on mlr3. Inspired by the summary output of (generalized) linear models.

Installation

You can install the development version of mlr3summary:

{r eval = FALSE} remotes::install_github("mlr-org/mlr3summary")

If you want to get started with mlr3 (the basis of mlr3summary), we recommend installing the mlr3verse meta-package which installs mlr3 and some of the most important extension packages: {r eval = FALSE} install.packages("mlr3verse") library(mlr3verse)

Example

Load data and create a task

library(mlr3summary)
data("credit", package = "mlr3summary")
task = TaskClassif$new(id = "credit", backend = credit, target = "risk", positive = "good")

Fit a model and resampling strategy

set.seed(12005L)
rf = lrn("classif.ranger", predict_type = "prob")
rf$train(task)

cv3 = rsmp("cv", folds = 3L)
rr = resample(task = task, learner = rf, resampling = cv3, store_models = TRUE)
rr$aggregate(msrs(list("classif.acc", "classif.auc")))

Apply the summary function

summary(object = rf, resample_result = rr)

More examples can be found in demo/.

Citation

If you use mlr3summary, please cite:

Dandl S, Becker M, Bischl B, Casalicchio G, Bothmann L (2024).
mlr3summary: Model and learner summaries for 'mlr3'.
R package version 0.1.0.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {mlr3summary: Model and learner summaries for 'mlr3'},
    author = {Susanne Dandl and Marc Becker and Bernd Bischl and Giuseppe Casalicchio and Ludwig Bothmann},
    year = {2024},
    note = {R package version 0.1.0},
  }

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.
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