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Minority class: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In [[machine learning]], the [[minority class]] is the less common [[label]] in a [[class-imbalanced dataset|dataset that is imbalanced]]. For example, in a [[dataset]] where there are 80% "yes" and 20% "no", "no" is the minority [[class]]. The opposite of the majority class the [[majority class]]. | |||
A [[classification model]] trained on an [[class-imbalanced dataset]] is likely to be [[biased]] towards the majority class and may not be able to [[accurately]] predict the minority one. In real-life examples, the minority class often holds greater interest due to its representation of important target variables such as fraud detection or disease diagnosis. | |||
==Handling Minority Class in Machine Learning== | ==Handling Minority Class in Machine Learning== | ||
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Machine learning often presents the situation where we have a wealth of data about one group but not nearly enough about another. This minority group, also referred to as the minority population, must be taken into consideration; otherwise, our models may not work as effectively for them due to insufficient information. Therefore, we need to ensure our models are fair and beneficial for both majorities and minorities alike. | Machine learning often presents the situation where we have a wealth of data about one group but not nearly enough about another. This minority group, also referred to as the minority population, must be taken into consideration; otherwise, our models may not work as effectively for them due to insufficient information. Therefore, we need to ensure our models are fair and beneficial for both majorities and minorities alike. | ||
==The Challenge of Minority Class== | ==The Challenge of Minority Class== | ||