A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
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curl https://depscope.dev/api/check/conda/r-unbalancedFirst published · 2020-05-29 07:49:45.841000+00:00
Last updated · 2025-09-18 06:28:04.051000+00:00