README.md

In mlr3summary: Model and Learner Summaries for 'mlr3'

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](https://mlr3verse.mlr-org.com/) 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},
  }

mlr3summary documentation built on May 29, 2024, 2:44 a.m.