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035   $a(PPN)232966893
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100   $a20181213d2019      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMachine Learning Risk Assessments in Criminal Justice Settings /$fby Richard Berk
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (184 pages)
311   $a3-030-02271-4 
320   $aIncludes bibliographical references and index.
327   $a1 Getting Started -- 2 Some Important Background Material -- 3 A Conceptual Introduction Classification and Forecasting -- 4 A More Formal Treatment of Classification and Forecasting -- 5 Tree-Based Forecasting Methods -- 6 Transparency, Accuracy and Fairness -- 7 Real Applications -- 8 Implementation -- 9 Some Concluding Observations About Actuarial Justice and More.
330   $aThis book puts in one place and in accessible form Richard Berk?s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than ?predictive policing? for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.
606   $aArtificial intelligence
606   $aMathematical statistics
606   $aCriminology
606   $aResearch
606   $aData mining
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036
606   $aQuantitative Criminology$3https://scigraph.springernature.com/ontologies/product-market-codes/1BF010
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
615  0$aArtificial intelligence.
615  0$aMathematical statistics.
615  0$aCriminology.
615  0$aResearch.
615  0$aData mining.
615 14$aArtificial Intelligence.
615 24$aProbability and Statistics in Computer Science.
615 24$aQuantitative Criminology.
615 24$aData Mining and Knowledge Discovery.
676   $a364.22
700   $aBerk$b Richard$4aut$4http://id.loc.gov/vocabulary/relators/aut$01064222
906   $aBOOK
912   $a9910337835903321
996   $aMachine Learning Risk Assessments in Criminal Justice Settings$92536956
997   $aUNINA