A Conceptual Introduction to Classification and Forecasting
Because the criminal justice outcomes to be forecast are usually categorical (e.g., fail or not), this chapter considers crime forecasting as a classification problem. The goal is to assign classes to cases. There may be two classes or more than two. Machine learning is broadly considered before turning in later chapters to random forests as a preferred forecasting tool. There is no use of models and at best a secondary interest in explanation. Machine learning is based on algorithms, which should not be confused with models. The material is introduced in a conceptual manner with almost no mathematics. Nevertheless, some readers may find the material challenging because a certain amount of statistical maturity must be assumed. Later chapters will use somewhat more formal expositional methods.