interpretable statistics

An interpretable drift adaptation mechanism for detection and correction is introduced, based on statistical tests and transparent transformations.  In contrast to prior work that applies a single universal mapping, the method adaptively selects transformations by drift type (location, scale, shape, or extreme), identified via Kolmogorov–Smirnov tests, Wasserstein distance, and distributional comparisons.  Each category is corrected with a suitable transformation such as mean-variance scaling, rank-based adjustment, or quantile mapping.  A novel Wasserstein–aware fallba