This refers to a special case of discretization in which a continuous variable is transformed into a categorical representation that only consists of two bins. Thus, a dummy variable is essentially created, where one bin represents the “on” level and the other the “off” or reference level. This approach works well when there is a near dichotomous threshold that separates observations in relation to the target and also adds fewer dimensions to the feature space compared to if more bins are created.
What is Feature Binarization? When to use feature binarization?
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