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
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 The Author
About this chapter
Cite this chapter
Berk, R. (2012). Tree-Based Forecasting Methods. In: Criminal Justice Forecasts of Risk. SpringerBriefs in Computer Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3085-8_5
Download citation
DOI: https://doi.org/10.1007/978-1-4614-3085-8_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-3084-1
Online ISBN: 978-1-4614-3085-8
eBook Packages: Computer ScienceComputer Science (R0)