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Implementing Prediction: Overcoming the Many Challenges in a Real-World Setting

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Abstract:

New and powerful statistical techniques offer an unprecedented opportunity to make use of the wealth of data that most criminal justice agencies already have on hand. But these data are frequently scattered across many different data sources, with numerous gatekeepers, a variety of different formats and coding rules, and an almost overwhelming list of limitations on the accuracy of the data. This paper describes how a random forest prediction model was developed and implemented in Philadelphia's Adult Probation and Parole Department. It includes a description of what random forest modeling does, how the data sources were brought together to build the model, and how the entire process was made available on the department's computer network, allowing for instantaneous predictions about the anticipated criminal behavior of incoming probationers.
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Association:
Name: ASC Annual Meeting
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http://www.asc41.com


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URL: http://citation.allacademic.com/meta/p373276_index.html
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MLA Citation:

Barnes, Geoffrey. "Implementing Prediction: Overcoming the Many Challenges in a Real-World Setting" Paper presented at the annual meeting of the ASC Annual Meeting, Philadelphia Marriott Downtown, Philadelphia, PA, <Not Available>. 2014-11-28 <http://citation.allacademic.com/meta/p373276_index.html>

APA Citation:

Barnes, G. C. "Implementing Prediction: Overcoming the Many Challenges in a Real-World Setting" Paper presented at the annual meeting of the ASC Annual Meeting, Philadelphia Marriott Downtown, Philadelphia, PA <Not Available>. 2014-11-28 from http://citation.allacademic.com/meta/p373276_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Abstract: New and powerful statistical techniques offer an unprecedented opportunity to make use of the wealth of data that most criminal justice agencies already have on hand. But these data are frequently scattered across many different data sources, with numerous gatekeepers, a variety of different formats and coding rules, and an almost overwhelming list of limitations on the accuracy of the data. This paper describes how a random forest prediction model was developed and implemented in Philadelphia's Adult Probation and Parole Department. It includes a description of what random forest modeling does, how the data sources were brought together to build the model, and how the entire process was made available on the department's computer network, allowing for instantaneous predictions about the anticipated criminal behavior of incoming probationers.


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