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  • Search: subject:"Extreme Gradient Boosting"
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Year of publication
Subject
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Artificial intelligence 8 Künstliche Intelligenz 8 Forecasting model 7 Prognoseverfahren 7 machine learning 5 Extreme Gradient Boosting 4 extreme gradient boosting 4 Industry 4.0 3 Mustererkennung 3 Neural networks 3 Neuronale Netze 3 Pattern recognition 3 artificial intelligence 3 eXtreme Gradient Boosting (XGBoost) 3 high-tech companies 3 random forest 3 robotics 3 Credit rating 2 Croatia 2 Ensemble 2 High technology 2 Hochtechnologie 2 Industrie 4.0 2 Insolvency 2 Insolvenz 2 Kreditwürdigkeit 2 Kroatien 2 Lasso 2 Logit 2 Regression analysis 2 Regressionsanalyse 2 Ridge 2 Robot 2 Roboter 2 SMEs 2 Support Vector Machine 2 bankruptcy 2 deep learning 2 impacts of I4.0 on business results 2 multiple abortions 2
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Online availability
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Free 13 CC license 6
Type of publication
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Article 8 Book / Working Paper 5
Type of publication (narrower categories)
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Article in journal 6 Aufsatz in Zeitschrift 6 Working Paper 5 Arbeitspapier 3 Graue Literatur 3 Non-commercial literature 3 Article 2
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Language
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English 13
Author
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Grebenar, Tomislav 3 Hrbić, Rajka 3 Brédart, Xavier 2 Kumar, Pradeep 2 Musa, Mohamed 2 Nicodemo, Catia 2 Oreffice, Sonia 2 Quintana-Domeque, Climent 2 Shetty, Shekar 2 Abdelmoniem, Ahmed M. 1 Akyildirim, Erdinc 1 Bairagi, Anupam Kumar 1 Barth, Michael 1 Cepni, Oguzhan 1 Corbet, Shaen 1 Emrich, Eike 1 Gadgil, Karan 1 Galibuzzaman 1 Gill, Sukhpal Singh 1 Güllich, Arne 1 Hassan, Md. Mahedi 1 Hassan, Md. Mehedi 1 Islam, Khan Kamrul 1 Khan, Md. Asif Rakib 1 Krasovytskyi, Danylo 1 Praiya Panjee 1 Sataporn Amornsawadwatana 1 Stavytskyy, Andriy 1 Uddin, Mohammed Gazi Salah 1 Yasmin, Farhana 1 Zaman, Sadika 1
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Published in...
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Journal of Risk and Financial Management 2 Journal of risk and financial management : JRFM 2 Decision analytics journal 1 Discussion paper series / IZA 1 Diskussionspapiere des Europäischen Instituts für Sozioökonomie e.V. 1 IZA Discussion Papers 1 Journal of economy and technology 1 Mokslo darbai / Vilniaus Universitetas 1 Risks : open access journal 1 Working paper / Department of Economics, Copenhagen Business School 1 Working papers / Croatian National Bank 1
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Source
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ECONIS (ZBW) 9 EconStor 4
Showing 1 - 10 of 13
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Machine Learning and Multiple Abortions
Kumar, Pradeep; Nicodemo, Catia; Oreffice, Sonia; … - 2024
in the highest risk decile, capturing about 55% of cases, whereas linear models and Extreme Gradient Boosting excel in …This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient … Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and …
Persistent link: https://www.econbiz.de/10015045482
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A generalized linear model and machine learning approach for predicting the frequency and severity of cargo insurance in Thailand's border trade context
Praiya Panjee; Sataporn Amornsawadwatana - In: Risks : open access journal 12 (2024) 2, pp. 1-33
to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost … slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better … error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error …
Persistent link: https://www.econbiz.de/10014497395
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Machine learning and multiple abortions
Kumar, Pradeep; Nicodemo, Catia; Oreffice, Sonia; … - 2024
in the highest risk decile, capturing about 55% of cases, whereas linear models and Extreme Gradient Boosting excel in …This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient … Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and …
Persistent link: https://www.econbiz.de/10014545133
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Predicting mortgage loan defaults using machine learning techniques
Krasovytskyi, Danylo; Stavytskyy, Andriy - In: Mokslo darbai / Vilniaus Universitetas 103 (2024) 2, pp. 140-160
oversampling technique and compared the results. It was found that random forest and extreme gradient-boosting decision trees are …
Persistent link: https://www.econbiz.de/10015047688
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A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction
Hassan, Md. Mehedi; Hassan, Md. Mahedi; Yasmin, Farhana; … - In: Decision analytics journal 7 (2023), pp. 1-17
) approach, which selects the most important attributes. Logistic Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient … Boosting (XGB), Gradient Boosting (GB), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM …
Persistent link: https://www.econbiz.de/10014497358
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A meta-learning based stacked regression approach for customer lifetime value prediction
Gadgil, Karan; Gill, Sukhpal Singh; Abdelmoniem, Ahmed M. - In: Journal of economy and technology 1 (2023), pp. 197-207
Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more. Customer lifetime value (CLV) is the total monetary value of transactions or purchases made by a customer with the...
Persistent link: https://www.econbiz.de/10014555514
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Assessment of readiness of Croatian companies to ontroduce I4.0 technologies
Hrbić, Rajka; Grebenar, Tomislav - In: Journal of Risk and Financial Management 15 (2022) 12, pp. 1-24
indicators of a sample of 58 identified I4.0 companies. We developed a machine-learning model by using the eXtreme Gradient … Boosting algorithm (XGBoost) for this purpose, an approach that has not been used in any similar research. This research shows …
Persistent link: https://www.econbiz.de/10014332714
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Bankruptcy prediction using machine learning techniques
Shetty, Shekar; Musa, Mohamed; Brédart, Xavier - In: Journal of Risk and Financial Management 15 (2022) 1, pp. 1-10
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost …
Persistent link: https://www.econbiz.de/10013201339
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Bankruptcy prediction using machine learning techniques
Shetty, Shekar; Musa, Mohamed; Brédart, Xavier - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-10
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost …
Persistent link: https://www.econbiz.de/10012814176
Saved in:
Cover Image
Assessment of readiness of Croatian companies to ontroduce I4.0 technologies
Hrbić, Rajka; Grebenar, Tomislav - In: Journal of risk and financial management : JRFM 15 (2022) 12, pp. 1-24
indicators of a sample of 58 identified I4.0 companies. We developed a machine-learning model by using the eXtreme Gradient … Boosting algorithm (XGBoost) for this purpose, an approach that has not been used in any similar research. This research shows …
Persistent link: https://www.econbiz.de/10014284413
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