| Description |
1 online resource (ix, 178 pages) |
| Contents |
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 |
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2.2.3 Representative Data2.2.4 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 |
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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 (GX) 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 |
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5.2 Once More on Constructing Data Partitions5.3 Building the Costs of Classification Errors; 5.4 Confusion Tables; 5.5 Ensembles of Classification Trees: Random Forests; 5.5.1 Transfer Learning; 5.5.2 Variable Importance for Random Forests; 5.5.3 Partial Response Functions from Random Forests; 5.5.4 Forecasting; 5.5.4.1 Forecasting Accuracy for Random Forests; 5.5.4.2 Reliability of Random Forest Forecasts; 5.5.5 Statistical Inference for Random Forests; 5.5.6 Conclusions About Random Forests; 5.6 Tree-Based Alternatives to Random Forests; 5.6.1 Stochastic Gradient Boosting |
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5.6.2 Bayesian Additive Regression Trees5.7 Why Do Ensembles of Trees Work So Well?; 5.7.1 Neural Networks and Deep Learning; 6 Transparency, Accuracy and Fairness; 6.1 Introduction; 6.2 Transparency; 6.3 Accuracy; 6.4 Fairness; 6.5 Improving Data Quality; 6.6 Communicating with Stakeholders; 6.7 A Policy Suggestion; 7 Real Applications; 7.1 A Simplified Example; 7.2 A More Complex Example; 7.3 Some Fairness Tradeoffs; 8 Implementation; 8.1 Making It Real; 8.2 Demonstration Phase; 8.3 Hardware and Software; 8.4 Data Preparation for Forecasting; 8.5 Personnel; 8.6 Public Relations |
| Summary |
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 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 |
| Notes |
Online resource; title from digital title page (viewed on February 12, 2019) |
| Subject |
Recidivism -- Forecasting -- Data processing
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Machine learning.
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Machine learning
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| Form |
Electronic book
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| ISBN |
9783030022723 |
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3030022722 |
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9783030022730 |
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3030022730 |
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