Summary
Machine learning originated at the intersection of statistics, database science, and computer science. It is a powerful tool, capable of finding actionable insight in large quantities of data. Still, as we have seen in this chapter, caution must be used in order to avoid common abuses of machine learning in the real world.
Conceptually, the learning process involves the abstraction of data into a structured representation, and the generalization of the structure into action that can be evaluated for utility. In practical terms, a machine learner uses data containing examples and features of the concept to be learned, then summarizes this data in the form of a model, which is used for predictive or descriptive purposes. These purposes can be grouped into tasks including classification, numeric prediction, pattern detection, and clustering. Among the many possible methods, machine learning algorithms are chosen based on the input data and the learning task.
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