Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models
TLDR
In this article, prediction mod els are developed that predict three types of criminal recidivism: general recidivisitc, violent re-conviction, and sexual recidivasitc.Abstract:
Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared on a large selection of performance measures. Results indicate that classical methods do equally well as or better than their modern counterparts. The predictive performance of the various techniques differs only slightly for general and violent recidivism, whereas differences are larger for sexual recidivism. For the general and violent recidivism data we present the results of logistic regression and for sexual recidivism of linear discriminant analysis. Language: enread more
Citations
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
TL;DR: This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications whereinterpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.
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Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
TL;DR: In this article, the chasm between explaining black box models and using inherently interpretable models is identified, and several key reasons why explainable models should be avoided in high-stakes decisions.
Posted Content
Please Stop Explaining Black Box Models for High Stakes Decisions.
TL;DR: There is a way forward – it is to design models that are inherently interpretable, rather than trying to explain black box models, which is likely to perpetuate bad practices and can potentially cause catastrophic harm to society.
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Interpretable Classification Models for Recidivism Prediction
TL;DR: A recent method called supersparse linear integer models is used to produce accurate, transparent and interpretable scoring systems along the full ROC curve, which can be used for decision making for many different use cases.
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Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron.
TL;DR: This dataset, intended to be a time-series dataset, is transformed into a regression dataset and used in training a multilayer perceptron (MLP) artificial neural network (ANN) to achieve a worldwide model of the maximal number of patients across all locations in each time unit.
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