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Unbiased assessment of the predictivity of models learnt by supervisedmachine-learning methods requires knowledge of the learned function over areserved test set (not used by the learning algorithm). The quality of theassessment depends, naturally, on the properties of the test set and on theerror statistic used to estimate the prediction error. In this work we tackleboth issues, proposing a new predictivity criterion that carefully weights theindividual observed errors to obtain a global error estimate, and usingincremental experimental design methods to "optimally" select the test pointson which the criterion is computed. Several incremental constructions arestudied, including greedy-packing (coffee-house design), support points andkernel herding techniques. Our results show that the incremental and weightedversions of the latter two, based on Maximum Mean Discrepancy concepts, yieldsuperior performance. An industrial test case provided by the historical Frenchelectricity supplier (EDF) illustrates the practical relevance of themethodology, indicating that it is an efficient alternative to expensivecross-validation techniques.

Model predictivity assessment: incremental test-set selection and accuracy evaluation