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  • Search: subject:"interpretability of machine learning"
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Year of publication
Subject
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early warning system 3 interpretability of machine learning 3 predictive performance 3 Artificial intelligence 2 Early warning system 2 Forecasting model 2 Frühwarnsystem 2 Künstliche Intelligenz 2 Logit model 2 Logit-Modell 2 Prognoseverfahren 2 fiscal stress 1
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Online availability
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Free 2 Undetermined 1
Type of publication
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Book / Working Paper 2 Article 1
Type of publication (narrower categories)
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Working Paper 2 Arbeitspapier 1 Article in journal 1 Aufsatz in Zeitschrift 1 Graue Literatur 1 Non-commercial literature 1
Language
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English 3
Author
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Jarmulska, Barbara 3
Published in...
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ECB Working Paper 1 Journal of forecasting 1 Working paper series / European Central Bank 1
Source
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ECONIS (ZBW) 2 EconStor 1
Showing 1 - 3 of 3
Cover Image
Random forest versus logit models: Which offers better early warning of fiscal stress?
Jarmulska, Barbara - 2020
This study seeks to answer whether it is possible to design an early warning system framework that can signal the risk of fiscal stress in the near future, and what shape such a system should take. To do so, multiple models based on econometric logit and the random forest models are designed and...
Persistent link: https://www.econbiz.de/10012422070
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Cover Image
Random forest versus logit models : which offers better early warning of fiscal stress?
Jarmulska, Barbara - 2020
This study seeks to answer whether it is possible to design an early warning system framework that can signal the risk of fiscal stress in the near future, and what shape such a system should take. To do so, multiple models based on econometric logit and the random forest models are designed and...
Persistent link: https://www.econbiz.de/10012216574
Saved in:
Cover Image
Random forest versus logit models : which offers better early warning of fiscal stress?
Jarmulska, Barbara - In: Journal of forecasting 41 (2022) 3, pp. 455-490
Persistent link: https://www.econbiz.de/10013166154
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