Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but its thresholding step makes validation rankings depend on the chosen cutoff. SoftMCC is a post-training MCC validation framework built on established probability-valued confusion counts, coupling an MCC-specific calibrated identity with a tie-aware, shared-pool selection protocol. Its core score is a covariance-normalized probability-label association, reduces exactly to MCC for hard predictions, and is Pearson-bounded. Under perfect population calibration it equals the Brier skill score with identical candidate ordering, which is the calibrated case of Murphy's partition of the Brier score. Outside that regime the gap does not identify calibration error. Across 18 settings with 12 duplicate-safe grouped repeats, SoftMCC attains the best stability mean rank (2.31) and highest mean tie-corrected Kendall's W (0.659), with a significant Friedman test (p=0.004). Nemenyi analysis separates it from area under the precision-recall curve (AUPRC) and [email protected], and 14-source-family sensitivity retains both separations. For selected-model utility, none of six prespecified paired comparisons is significant before or after Holm correction (smallest raw p=0.093), and the dataset-level test is not significant (p=0.746). Label permutation lowers mean W to 0.092. Temperature scaling shifts SoftMCC rankings (mean Spearman 0.851) whereas rank-based and threshold-optimized metrics remain invariant. SoftMCC is a calibration-sensitive MCC-family selector with bounded stability evidence and inconclusive utility differences.

翻译：暂无翻译