GRASP: A Goodness-of-Fit Test for Classification Learning
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Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterizing the fit of the model to the underlying conditional law of labels given the features vector (Y|X), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric assumption on the conditional law Y|X, and treats that as a black box oracle model which can be accessed only through queries. We formulate the goodness-of-fit assessment problem as a tolerance hypothesis testing of the form H_0: 𝔼[D_f( Bern(η(X)) Bern(η̂(X)))]≤τ , where D_f represents an f-divergence function, and η(x), η̂(x) respectively denote the true and an estimate likelihood for a feature vector x admitting a positive label. We propose a novel test, called for testing H_0, which works in finite sample settings, no matter the features (distribution-free). We also propose model-X designed for model-X settings where the joint distribution of the features vector is known. Model-X uses this distributional information to achieve better power. We evaluate the performance of our tests through extensive numerical experiments.
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