auc: Area under the ROC curve

In spatialRF: Easy Spatial Modeling with Random Forest

Area under the ROC curve

Description

Computes the area under the ROC curve (AUC) for binary classification.

Usage

auc(o, p)

Arguments

o	Numeric vector of actual binary labels (0 or 1). Must have the same length as p.
p	Numeric vector of predicted probabilities (typically 0 to 1). Must have the same length as o.

Value

Numeric value between 0 and 1 representing the AUC. Higher values indicate better classification performance, with 0.5 indicating random performance and 1.0 indicating perfect classification.

See Also

Other utilities: .vif_to_df(), beowulf_cluster(), objects_size(), optimization_function(), prepare_importance_spatial(), rescale_vector(), root_mean_squared_error(), setup_parallel_execution(), standard_error(), statistical_mode(), thinning(), thinning_til_n()

Examples

auc(
  o = c(0, 0, 1, 1),
  p = c(0.1, 0.6, 0.4, 0.8)
  )

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.