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Filtering by: Creator Tanaree, D. D. Degree Level Doctoral Resource Type Technical Report

Model Selection of Discrete Classifiers under Class and Cost Distribution Change: An Empirical Study

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:

bounded as 0 ≤ D ≤ √ 2, with 0 being a perfect selection. 5 Results We present a set of representative