discriminator_auc: Discriminator AUC

In SynthesizerPlus: Synthetic Data Generation with Copulas, Evaluation and Multi-Format I/O

Discriminator AUC

Description

Cross-validated area under the ROC curve of a logistic-regression classifier trained to tell real from synthetic records. Out-of-fold predictions are used, and all preprocessing (imputation, scaling, choice of categories) is learned within the training folds, so the AUC is not optimistically biased. Values near 0.5 mean the classifier cannot distinguish the data sets; values near 1 mean the synthetic data are easy to spot.

Usage

discriminator_auc(
  real,
  synthetic,
  vars = NULL,
  folds = 5L,
  max_rows = 10000L,
  seed = NULL,
  model = c("quadratic", "linear")
)

Arguments

real, synthetic	Data frames with common columns.
vars	Optional subset of variables.
folds	Number of cross-validation folds.
max_rows	Each data set is subsampled to at most this many rows.
seed	Optional random seed (subsampling).
model	Classifier features: "quadratic" (default) or "linear".

Details

Values clearly below 0.5 are a warning sign of a different kind: they arise when synthetic records are copies of real ones. A held-out record then has an identical twin with the opposite label in the training folds, and the classifier systematically predicts the wrong class. Check such cases with dcr(). The AUC is therefore deliberately not folded into a symmetric measure such as \max(AUC, 1 - AUC): that would report copies of the real data as easily distinguishable.

The default model = "quadratic" adds squares and pairwise products of the numeric variables (ridge-penalised), so that differences in spread and in correlation are detected, not only differences in location. model = "linear" uses main effects only.

Value

The cross-validated AUC (a number).

Examples

syn <- synthesize(iris, seed = 1)
discriminator_auc(iris, syn, seed = 1)

SynthesizerPlus documentation built on Oct. 16, 2026, 5:10 p.m.