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  • Search: subject:"Random Forest algorithm"
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Year of publication
Subject
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random forest algorithm 5 Artificial intelligence 4 Künstliche Intelligenz 4 Algorithm 2 Algorithmus 2 Forecasting model 2 Forestry 2 Forstwirtschaft 2 Machine learning 2 Prognoseverfahren 2 Random Forest algorithm 2 Random forest algorithm 2 Theorie 2 Theory 2 Altenhilfe 1 Athletes 1 Ball game 1 Ballsport 1 Bankruptcy 1 Bergbau 1 Betriebliche Liquidität 1 COVID-19 Crisis 1 China 1 Cluster analysis 1 Clusteranalyse 1 Consumer behaviour 1 Consumer credit 1 Coronavirus 1 Corporate liquidity 1 Crime 1 Crime against public health 1 Crime prevention 1 Criminal policy 1 Crisis management 1 Data Mining 1 Data mining 1 Decision tree 1 Demand 1 Digital platform 1 Digitale Plattform 1
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Online availability
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Undetermined 6 Free 3 CC license 1
Type of publication
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Article 8 Book / Working Paper 1
Type of publication (narrower categories)
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Article in journal 8 Aufsatz in Zeitschrift 8 Arbeitspapier 1 Graue Literatur 1 Non-commercial literature 1 Working Paper 1
Language
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English 9
Author
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Bozkurt, İbrahim 1 Gadasina, Lyudmila 1 Jia, Chaoyong 1 Kaempff, Bob 1 Kang, Lili 1 Kaya, Muhammed Veysel 1 Kremer, David 1 Luukka, Pasi 1 Ma, Sanjun 1 Prakash, C. Deep 1 Verma, Sanjay 1 Voitenko, Sergey 1 Wang, Hongning 1 Xiao, Tao 1 Yang, Ming 1 Zhang, Ning 1 Zhang, Tao 1 Zhao, Guangchuan 1
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Published in...
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International journal of product development : IJPD 2 Applied economics letters 1 Cahiers d'etudes / Banque Centrale du Luxembourg 1 Decision analytics journal 1 Finance research letters 1 International journal of networking and virtual organisations : IJNVO 1 Nordic journal of business : NJB 1 Socio-economic planning sciences : the international journal of public sector decision-making 1
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Source
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ECONIS (ZBW) 9
Showing 1 - 9 of 9
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Using machine learning to aggregate apartment prices : comparing the performance of different Luxembourg indices
Kaempff, Bob; Kremer, David - 2025
Persistent link: https://www.econbiz.de/10015197300
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A new in-form and role-based Deep Player Performance Index for player evaluation in T20 Cricket
Prakash, C. Deep; Verma, Sanjay - In: Decision analytics journal 2 (2022)
use the modified guidelines in the context of T20 Cricket. We propose DPPI based on K-Means clustering and Random Forest … algorithm and compare our results with the existing player performance evaluation indexes for the Indian Premier League (IPL …
Persistent link: https://www.econbiz.de/10013163119
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Prediction method of product market demand based on Prophet random forest
Jia, Chaoyong - In: International journal of product development : IJPD 28 (2024) 1/2, pp. 60-72
Persistent link: https://www.econbiz.de/10015065285
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Consumer credit evaluation of mobile e-commerce platform based on random forest
Yang, Ming - In: International journal of networking and virtual … 29 (2023) 3/4, pp. 272-284
Persistent link: https://www.econbiz.de/10015066176
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Foremost features affecting financial distress and Bankruptcy in the acute stage of COVID-19 crisis
Bozkurt, İbrahim; Kaya, Muhammed Veysel - In: Applied economics letters 30 (2023) 8, pp. 1112-1123
Persistent link: https://www.econbiz.de/10014303740
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The digital diversity in Russian regional dynamics : analysis by machine learning methods
Gadasina, Lyudmila; Voitenko, Sergey; Luukka, Pasi - In: Nordic journal of business : NJB 69 (2020) 1, pp. 7-20
Persistent link: https://www.econbiz.de/10012305134
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Financial support for unmet need for personal assistance with daily activities : implications from China's long-term care insurance pilots
Kang, Lili; Zhao, Guangchuan - In: Finance research letters 45 (2022), pp. 1-8
Persistent link: https://www.econbiz.de/10014581719
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Preventing crimes against public health with artificial intelligence and machine learning capabilities
Wang, Hongning; Ma, Sanjun - In: Socio-economic planning sciences : the international … 80 (2022), pp. 1-8
Persistent link: https://www.econbiz.de/10013191228
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Green production cycle mining of mass production based on random forest algorithm
Xiao, Tao; Zhang, Tao; Zhang, Ning - In: International journal of product development : IJPD 24 (2020) 2/3, pp. 182-199
Persistent link: https://www.econbiz.de/10012516100
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