Machine Learning Risk Assessments in Criminal Justice Settings
Intro -- Preface -- Contents -- 1 Getting Started -- 1.1 Some Introductory Caveats -- 1.2 What Criminal Justice Risk Assessment Is and Is Not -- 1.3 Difficulties Defining Machine Learning -- 1.4 Setting the Stage -- 1.4.1 A Brief Motivating Example -- 2 Some Important Background Material -- 2.1 Policy Considerations -- 2.1.1 Criminal Justice Risk Assessment Goals -- 2.1.2 Decisions to Be Informed by the Forecasts -- 2.1.3 Outcomes to Be Forecasted -- 2.1.4 Real World Constraints -- 2.1.5 Stakeholders -- 2.2 Data Considerations -- 2.2.1 Stability Over Time -- 2.2.2 Training Data, Evaluation Data, and Test Data -- 2.2.3 Representative Data -- 2.2.4 Large Samples -- 2.2.5 Data Continuity -- 2.2.6 Missing Data -- 2.3 Statistical Considerations -- 2.3.1 Actuarial Methods -- 2.3.2 Building in the Relative Costs of Forecasting Errors -- 2.3.3 Effective Forecasting Algorithms -- 3 A Conceptual Introduction to Classification and Forecasting -- 3.1 Populations and Samples -- 3.2 Classification and Forecasting Using Decision Boundaries -- 3.3 Classification by Data Partitions -- 3.4 Forecasting by Data Partitions -- 3.5 Finding Good Data Partitions -- 3.6 Enter Asymmetric Costs -- 3.7 Recursive Partitioning Classification Trees -- 3.7.1 How Many Terminal Nodes? -- 3.7.2 Classification Tree Instability and Adaptive Fitting -- 4 A More Formal Treatment of Classification and Forecasting -- 4.1 Introduction -- 4.2 What Is Being Estimated? -- 4.3 Data Generation Formulations -- 4.4 Notation -- 4.5 From Probabilities to Classification -- 4.6 Computing (G|X) in the Real World -- 4.6.1 Estimation Bias -- 4.6.2 The Traditional Bias-Variance Tradeoff with Extensions -- 4.6.3 Addressing Uncertainty -- 4.7 A Bit More on the Joint Probability Model -- 4.8 Putting It All Together -- 5 Tree-Based Forecasting Methods -- 5.1 Introduction.
