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

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書目名稱Machine Learning Risk Assessments in Criminal Justice Settings讀者反饋學科排名

Condyle

Some Important Background Material,hat constructing statistical forecasts of criminal behavior is primarily a technical enterprise. But the forecasted risks inform real decisions by criminal justice officials and other stakeholders. As a result, a wide range of matters can arise, only some of which are technical. Although for exposit

HAVOC

A Conceptual Introduction to Classification and Forecasting,fication problem. The goal is to assign classes to cases. There may be two classes or more than two. Machine learning is broadly considered before turning in later chapters to random forests as a preferred forecasting tool. There is no use of models and at best a secondary interest in explanation. M

凹槽

A More Formal Treatment of Classification and Forecasting,of the model used to characterize how the data were generated. That model is very different from the one used in conventional regression. Then, classification is considered followed by the estimation issues it raises. The bias-variance tradeoff is front and center. So is post-selection statistical i

Ambiguous

Tree-Based Forecasting Methods,in criminal justice forecasting. The joint probability distribution model, data partitioning, and asymmetric costs should now be familiar. These features combine to make tree-based methods of recursive partitioning the fundamental building blocks for the machine learning procedures discussed. The ma

Fierce

Defiance

Real Applications,applications that led to procedures adopted by criminal justice agencies. As such, they combine a number of technical matters with the practical realities of criminal justice decisions-making. For the reasons already addressed, random forests will be the machine learning method of choice with one ex

Postulate

ENNUI

mercenary