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Filtering by: Creator Lamm, W. E. Advisor Hutchinson, Rebecca A. Resource Type Technical Report

Creator:: Ahmed, Nahian and Hutchinson, Rebecca A.
Abstract:: A variety of important machine learning applications require predictions on test data with different characteristics than the data on which a model was trained and validated. In particular, test data may have a different relative frequency of positives and negatives (i.e., class distribution) and/or different mislabeling costs of false positive...
Resource Type:: Technical Report
Full Text:: -Optimizing Model Selection Input: Metric Φ, changed environment conditions E, discrete classifiers S

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