Cross-Dataset Evaluation of Support Vector Machines: A Reproducible, Calibration-Aware Baseline for Tabular Classification - Nurus Syafi'ah; Mohammad Jamhuri; Farahnas Imaniyah Pranata; Ari Kusumastuti; Juhari Juhari; Usman Pagalay; Muhammad Khudzaifah (Universitas Islam Negeri Maulana Malik Ibrahim Malang, 2025)
Support Vector Machines (SVMs) remain competitive for small and medium-sized tabular classification problems, yet reported results on benchmark datasets vary widely due to inconsistent preprocessing, validation, and probability calibration. This paper presents a calibration-aware, cross-dataset benchmark that evaluates SVMs against classical baselines—Logistic Regression, Decision Tree, and Random Forest—under leakage-safe pipelines and statistically principled protocols. Using three representative binary datasets (Titanic survival, Pima Indians Diabetes, and UCI Heart Disease), we standardize imputation, encoding, scaling, and nested cross-validation to ensure comparability. Performance is assessed not only on discrimination metrics (accuracy, precision, recall, F1, PR–AUC) but also on probability reliability (Brier score, Expected Calibration Error) and threshold optimization. Results show that tuned RBF–SVMs consistently outperform Logistic Regression and Decision Trees, and perform comparably to Random Forests. Calibration (Platt scaling, isotonic regression) substantially reduces error and improves decision quality, while domain-specific features enhance Titanic prediction. By embedding all steps in a transparent, reproducible protocol and validating across multiple datasets, this study establishes a rigorous methodological baseline for SVMs in tabular binary classification, providing a reference point for future machine learning research.