Tree-Based Forecasting Methods

Abstract

The past two chapters have provided the necessary technical background for a consideration of statistical procedures that can be especially effective in criminal justice forecasting. The joint probability distribution model, data partitioning, and asymmetric costs should now be familiar. These features combine to make treebased methods the fundamental building blocks for the machine learning procedures discussed. The main focus is random forests. Stochastic gradient boosting and Bayesian trees are discussed briefly as worth competitors to random forests