Titlebook: Machine Learning Risk Assessments in Criminal Justice Settings; Richard Berk Book 2019 Springer Nature Switzerland AG 2019 Risk Assessment

GRILL

后來(lái)

sschnitte wie die dabei mitgeteilten Informationen offensichtlich auf das Ereignis der festgestellten Delinquenz des Sohnes beziehen, das ihnen die Selbstverst?ndlichkeiten des Alltags hat problematisch werden lassen, so da? aufgrund dieser ?Unselbstverst?ndlichkeit“ bei den Müttern ein Interesse an

干涉

A Conceptual Introduction to Classification and Forecasting,hematics. Nevertheless, some readers may find the material challenging because a certain amount of statistical maturity must be assumed. Later chapters will use somewhat more formal expositional methods.

AGGER

Tree-Based Forecasting Methods,ural nets and deep learning are not tree-based, they are also considered. Current claims about remarkable performance need to be dispassionately addressed, especially in comparison to tree-based methods.

ARM

Between

hout, difficult issues are clearly explained, supported by m.This 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 dataset

神經(jīng)

Some Important Background Material,ional purposes the issues must be examined one at a time, in practice each should be considered as part of a whole. Decisions about one necessarily affect decisions about another. Implied is the need to consider tradeoffs between competing priorities. We begin with some rather broad concerns and gradually narrow the discussion.

改變立場(chǎng)

acolyte

Transparency, Accuracy and Fairness,ent tools being deployed. The need to know includes transparency, accuracy, and fairness. All three raise complicated issues in part because they interact with one another. Each will be addressed in turn. There will be no technical fix and no easy answers.

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