Bibliometric Analysis and Visualization of Scientific Literature on Random Forest Regression

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Abstract
Random forest regression (RFR) is a versatile, easy-to-use and efficient tree based machine-learning algorithm that utilizes the power of multiple decision trees for making decisions. So random forest is a subject of a great deal of research, in many of the machine intelligence applications. The objective of this research is to investigate the scientific output of research based on RFR and to explore its hotspots and frontiers through bibliometric analysis for the years 2007 to 2019. The data are collected from the Web of Science database. The total number of publications, the citations, and types of publications, publication countries, productive authors, prominent journals, and keyword co-occurrence of RFR research are examined, using VOSviewer software. There are 516 papers, published in 299 journals, of which researchers from the USA published 162 articles. The most prolific author, with 6 publications and 240 citations, is Martin H. Teicher. The most cited article is the research article entitled "Genomic selection in wheat breeding using genotyping by sequencing". Among the journals, the most articles (41 publications) are published by the Remote Sensing journal.

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