BioMoR Benchmarking Tutorial

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

# 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)

## Loading required namespace: randomForest

## Loading required package: ggplot2

## Loading required package: lattice

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## 
## Attaching package: 'recipes'

## The following object is masked from 'package:stats':
## 
##     step

## randomForest 4.7-1.2

## Type rfNews() to see new features/changes/bug fixes.

## 
## Attaching package: 'randomForest'

## The following object is masked from 'package:dplyr':
## 
##     combine

## The following object is masked from 'package:ggplot2':
## 
##     margin

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

## Warning in bake(object$recipe, new_data = newdata, all_predictors()): ! There was 1 column that was a factor when the recipe was prepped:
## • `Label`
## ℹ This may cause errors when processing new data.
## ! There was 1 column that was a factor when the recipe was prepped:
## • `Label`
## ℹ This may cause errors when processing new data.

## Warning in confusionMatrix.default(y_pred, y_true): Levels are not in the same
## order for reference and data. Refactoring data to match.

## Setting direction: controls > cases

metrics

## $Accuracy
## [1] 1
## 
## $F1
## [1] 1
## 
## $ROC_AUC
## Area under the curve: 1

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