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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mlr3summary
Package website: [release](https://mlr3summary.mlr-org.com/) \| [dev](https://mlr3summary.mlr-org.com/dev/)
<!-- badges: start -->
[](https://github.com/mlr-org/mlr3summary/actions/workflows/rcmdcheck.yaml)
[](https://CRAN.R-project.org/package=mlr3summary)
[](https://stackoverflow.com/questions/tagged/mlr3)
[](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
<!-- badges: end -->
Concise, informative summaries of machine learning models.
Based on [mlr3](https://github.com/mlr-org/mlr3).
Inspired by the summary output of (generalized) linear models.
## Installation
Install the last release from CRAN:
```{r, eval = FALSE}
install.packages("mlr3summary")
```
Install the development version from GitHub:
```{r, eval = FALSE}
# install.packages("pak")
pak::pak("mlr-org/mlr3summary")
```
## Example
### Load data and create a task
```{r, eval = FALSE}
library(mlr3summary)
data("credit", package = "mlr3summary")
task = as_task_classif(credit, target = "risk", positive = "good")
```
### Fit a model and resampling strategy
```{r, eval = FALSE}
set.seed(12005L)
rf = lrn("classif.ranger", predict_type = "prob")
rf$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
```{r, eval = FALSE}
summary(object = rf, resample_result = rr)
```
More examples can be found in [demo/](https://github.com/mlr-org/mlr3summary/tree/master/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
```bibtex
@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}
}
```