000 | 03246nam a22005535i 4500 | ||
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001 | 978-3-030-02272-3 | ||
003 | DE-He213 | ||
005 | 20240423125118.0 | ||
007 | cr nn 008mamaa | ||
008 | 181213s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030022723 _9978-3-030-02272-3 |
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024 | 7 |
_a10.1007/978-3-030-02272-3 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aBerk, Richard. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aMachine Learning Risk Assessments in Criminal Justice Settings _h[electronic resource] / _cby Richard Berk. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aIX, 178 p. 32 illus., 27 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _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. | |
520 | _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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 | _aMathematical statistics. | |
650 | 0 | _aCriminology. | |
650 | 0 | _aData mining. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aProbability and Statistics in Computer Science. |
650 | 2 | 4 | _aResearch Methods in Criminology. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030022716 |
776 | 0 | 8 |
_iPrinted edition: _z9783030022730 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-02272-3 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c174375 _d174375 |