---
title: "BioMoR Benchmarking Tutorial"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{BioMoR Benchmarking Tutorial}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
library(BioMoR)
set.seed(123)
```

This vignette provides a short, fast example of benchmarking models with
**BioMoR**.

```{r}
# Prepare dataset
data(iris)
iris$Label <- ifelse(iris$Species == "setosa", "Active", "Inactive")

# Cross-validation control
ctrl <- get_cv_control(cv = 3)

# Train a Random Forest model
fit <- train_rf(iris, outcome_col = "Label", ctrl = ctrl)

# Compute simple benchmark metrics
metrics <- biomor_benchmark(fit, iris, outcome_col = "Label")
metrics
```

For more elaborate visualizations (ROC, PR curves, calibration plots), users can
combine the model predictions with packages such as **yardstick** and
**ggplot2**.