Study Benchmarks Post-Hoc Calibration Techniques Across Diverse Binary Classifiers

Global: Evaluation of Post-Hoc Calibration Methods for Binary Classification

Researchers have conducted a comprehensive benchmark of model-agnostic post‑hoc calibration methods aimed at improving probabilistic predictions in supervised binary classification on real i.i.d. tabular data. The study evaluates 21 widely used classifiers—including linear models, support vector machines, tree ensembles such as CatBoost, XGBoost, LightGBM, and contemporary tabular neural and foundation models—across binary tasks from the TabArena‑v0.1 suite, employing randomized, stratified five‑fold cross‑validation with a held‑out test fold.

Calibration Techniques Assessed

The analysis trains five calibrators—Isotonic regression, Platt scaling, Beta calibration, Venn‑Abers predictors, and Pearsonify—on a separate calibration split before applying them to test predictions. Evaluation metrics encompass proper scoring rules (log‑loss and Brier score), diagnostic measures (Spiegelhalter’s Z, Expected Calibration Error, and Expected Calibration Inaccuracy), as well as discrimination (AUC‑ROC) and standard classification metrics.

Key Performance Outcomes

Across the evaluated tasks and model architectures, Venn‑Abers predictors achieve the largest average reductions in log‑loss, closely followed by Beta calibration. Beta calibration most frequently improves log‑loss across tasks, whereas Venn‑Abers demonstrates fewer instances of extreme degradation and a slightly higher frequency of extreme improvement. In contrast, Platt scaling exhibits weaker and less consistent effects, and isotonic regression can systematically degrade proper scoring performance for strong modern tabular models.

Impact on Classification Accuracy

All calibration methods except Pearsonify marginally increase classification accuracy, though the effect remains modest; the greatest expected gain is approximately 0.008%. Discrimination, measured by AUC‑ROC, is generally preserved across calibrators, indicating that calibration primarily influences probability estimates rather than ranking performance.

Methodological Considerations

The study’s randomized, stratified five‑fold cross‑validation framework ensures robust assessment across diverse data distributions, while the separate calibration split isolates the influence of each calibrator. Nonetheless, calibration effects vary substantially across datasets and architectures, and no single method dominates uniformly.

Implications for Practitioners

Findings suggest that practitioners should prioritize Venn‑Abers or Beta calibration when seeking reliable reductions in log‑loss for binary tabular tasks, especially when working with modern, high‑performing models. Caution is advised when applying Platt scaling or isotonic regression to such models, as they may inadvertently worsen probabilistic calibration.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.