Estimating Model Performance Under Covariate Shift Without Labels

• URL: http://arxiv.org/abs/2401.08348v3
• Date: Tue, 28 May 2024 08:38:16 GMT
• Title: Estimating Model Performance Under Covariate Shift Without Labels
• Authors: Jakub Białek, Wojtek Kuberski, Nikolaos Perrakis, Albert Bifet,
• Abstract summary: We introduce Probabilistic Adaptive Performance Estimation (PAPE) for evaluating classification models on unlabeled data. PAPE provides more accurate performance estimates than other evaluated methodologies.
• Score: 9.804680621164168
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning models often experience performance degradation post-deployment due to shifts in data distribution. It is challenging to assess model's performance accurately when labels are missing or delayed. Existing proxy methods, such as drift detection, fail to measure the effects of these shifts adequately. To address this, we introduce a new method, Probabilistic Adaptive Performance Estimation (PAPE), for evaluating classification models on unlabeled data that accurately quantifies the impact of covariate shift on model performance. It is model and data-type agnostic and works for various performance metrics. Crucially, PAPE operates independently of the original model, relying only on its predictions and probability estimates, and does not need any assumptions about the nature of the covariate shift, learning directly from data instead. We tested PAPE on tabular data using over 900 dataset-model combinations created from US census data, assessing its performance against multiple benchmarks. Overall, PAPE provided more accurate performance estimates than other evaluated methodologies.

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