Machine learning models require input data, which consists of features or attributes that describe the characteristics or properties of the data points. These features can be numerical, categorical, or text-based, depending on the problem.

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Binary classification models take input data, which consists of a set of features or attributes. These features represent the characteristics or properties of the data points to be classified. The input data can be structured or unstructured, depending on the problem domain.