Model evaluation asks whether a system performs well enough for its intended use, on data representative of deployment, with known uncertainty and failure modes. Responsible evaluation is required for recommendation, automated decisions, predictive services, generative AI, safety-relevant systems, fraud detection, healthcare support, education technology, and any deployment affecting people or operations.
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Model evaluation asks whether a system performs well enough for its intended use, on data representative of deployment, with known uncertainty and failure modes.
supportedMetrics summarize specific properties and therefore reward different behavior.
supportedConfusion matrices, precision/recall, ROC or precision-recall curves, calibration plots, residual analysis, subgroup metrics, stress tests, drift measures, and real-world outcomes reveal distinct dimensions of system performance.