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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.

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.