Input
Concepts, instances, attributes
Abstract
Machine learning requires something to learn from: data. This chapter explains what kind of structure is required in the input data when applying the machine learning techniques covered in the book, and establishes the terminology that will be used. First, we explain what is meant by learning a concept from data, and describe the types of machine learning that will be considered: classification learning, association learning, clustering, and numeric prediction. We go on to explain what sort of examples a learning algorithm can be given to learn a concept from. “Examples” are described by attributes, and we continue by reviewing the types of attribute that are used. The final section of this ...
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