Adversarial Validation

Trains a binary classifier to distinguish training data from prediction locations. High classification accuracy (AUC > 0.7) indicates the prediction domain differs substantially from training, and spatial/blocked CV is essential. Low accuracy (AUC ~ 0.5) means random CV may suffice.

Usage

borg_adversarial(train, prediction, predictors = NULL, v = 5L)

Arguments

train

Data frame of training data.

prediction

Data frame of prediction locations (same predictor columns).

predictors

Character vector. If NULL, all shared numeric columns.

v

Integer. Number of CV folds for adversarial classifier. Default: 5.

Value

A list with class "borg_adversarial" containing:

auc

Cross-validated AUC of the adversarial classifier

dissimilarity

Dissimilarity score (0 to 100 percent)

recommendation

Suggested CV strategy based on dissimilarity

importance

Variable importance for distinguishing domains

Details

Uses logistic regression as the adversarial classifier (no external dependencies). The AUC is computed via cross-validation on the combined train+prediction dataset with a binary label (0=train, 1=prediction).

Interpretation:

• AUC < 0.6: Low dissimilarity. Random CV likely adequate.

• AUC 0.6-0.8: Moderate dissimilarity. Spatial CV recommended.

• AUC > 0.8: High dissimilarity. Spatial CV essential; check AOA.

Examples

set.seed(42)
train <- data.frame(a = rnorm(100), b = rnorm(100))
pred <- data.frame(a = rnorm(50, mean = 2), b = rnorm(50))
av <- borg_adversarial(train, pred)
av