A Conceptual Introduction to Classification and Forecasting

  • Richard Berk


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.


  1. Berk, R. A. (2016) Statistical Learning from a Regression Perspective second edition New York: Springer.CrossRefGoogle Scholar
  2. Breiman, L. (1996) Bagging predictors. Machine Learning 26:123–140.zbMATHGoogle Scholar
  3. Freedman, D.A. (2009) Statistical ModelsCambridge, UK: Cambridge University Press.Google Scholar
  4. Friedman, J. H. (2002) Stochastic gradient boosting. Computational Statistics and Data Analysis 38: 367–378.MathSciNetCrossRefGoogle Scholar
  5. Hastie, T., Tibshirani, R., & Friedman, J. (2009) The Elements of Statistical Learning. Second Edition. New York: Springer.CrossRefGoogle Scholar
  6. Ho, T.K. (1998) The random subspace method for constructing decision trees. IEEE Transactions on Pattern Recognition and Machine Intelligence 20 (8) 832–844.CrossRefGoogle Scholar
  7. Leeb, H., & Pötscher, B.M. (2005) Model selection and inference: facts and fiction,” Econometric Theory21: 21–59.Google Scholar
  8. Leeb, H., & Pötscher, B.M. (2006) Can one estimate the conditional distribution of post-model-selection estimators? The Annals of Statistics 34(5): 2554–2591.MathSciNetCrossRefGoogle Scholar
  9. Monahan, J., & Solver, E. (2003) Judicial decision thresholds for violence risk management. International Journal of Forensic Mental Health 2(1):1–6.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Richard Berk
    • 1
  1. 1.Department of CriminologyUniversity of PennsylvaniaPhiladelphiaUSA

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