Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance

Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance

Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. This study aimed to assess the influence of data features and overdispersion on RF regressio...

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Bibliographic Details
Main Authors:	Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï
Format:	Article
Language:	English
Published:	Wiley 2022-01-01
Series:	Journal of Probability and Statistics
Online Access:	http://dx.doi.org/10.1155/2022/2833537
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