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Imbalanced Data Classification
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Imbalanced data is a disproportionate number of data points with discrete labels and can be a big challenge to develop an accurate classifier. A classifier attempts to find the data boundary where one class ends and the other begins. [[Main/ClassificationOverview|Classification]] is used to create these boundaries when the desired output (label) is discrete such as 0/1, Yes/No, 1,2,3,4,5, or Normal/Abnormal. Regression is used when the desired output (label) is continuous.
Imbalanced data can give biased predictions for classification when there is a minority class. This tutorial demonstrates how to deal with imbalanced data.
Imbalanced data can give biased predictions for classification when there is a minority class. This tutorial demonstrates how to deal with imbalanced data.
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