Questions on recidivism prediction models

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Limuru institute of information and technology *

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COMPUTER F

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Marketing

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May 17, 2024

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docx

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3

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Questions on recidivism prediction models: 1. What is the primary objective of recidivism prediction models? a) To understand the root causes of criminal behavior b) To predict the likelihood of an individual reoffending c) To provide counseling to individuals at risk of reoffending d) To determine the sentence length for convicted individuals 2. Which of the following factors is commonly considered in recidivism prediction models? a) Socioeconomic status b) Political affiliation c) Favorite color d) Blood type 3. Which statistical technique is frequently used in developing recidivism prediction models? a) Linear regression b) Analysis of variance (ANOVA) c) Bayesian inference d) Genetic algorithm 4. True or False: Recidivism prediction models are always accurate in determining whether an individual will reoffend. a) True b) False
5. What is the term used to describe the percentage of correctly predicted cases by a recidivism prediction model? a) Precision b) Accuracy c) Sensitivity d) Specificity 6. Which of the following is an ethical concern associated with the use of recidivism prediction models? a) Privacy violations b) Lack of access to data c) Overestimation of risk d) Underestimation of risk 7. What type of data is commonly used to train recidivism prediction models? a) Only demographic data b) Only criminal history data c) Only psychological data d) Various types of data, including demographic, criminal history, and behavioral data 8. Which of the following is NOT a commonly used recidivism prediction model? a) LASSO regression b) Logistic regression c) Random forest d) K-means clustering 9. What is the main purpose of using recidivism prediction models in criminal justice systems?
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