An Ordinal Combination at a Contextual Action Boundary
Abstract
Combined predictions and contextual Gaussian-process allocation should be assessed where they change the selected alternative. A population average can hide a local rank reversal. Evaluation retains separate-model choices, fixes combination weights before checking, and reports sparse-context loss. Improved numerical prediction alone cannot establish that the combined contextual decision is useful. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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