ABC, ABC² and Gini scores fail to align with mean-consistent Bregman scoring rules because of predictor-dependent weights, so they can mislead point-forecast model selection.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Boosting trees test necessary conditions for calibration and auto-calibration of regression models, shown powerful on a large insurance dataset.
citing papers explorer
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Measures of predictive accuracy, miscalibration and discrimination
ABC, ABC² and Gini scores fail to align with mean-consistent Bregman scoring rules because of predictor-dependent weights, so they can mislead point-forecast model selection.
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Assessing model calibration with boosting trees
Boosting trees test necessary conditions for calibration and auto-calibration of regression models, shown powerful on a large insurance dataset.