Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data

Shahadat Uddin and Haohui Lu

PLOS ONE, 2024, vol. 19, issue 4, 1-12

Abstract: Many individual studies in the literature observed the superiority of tree-based machine learning (ML) algorithms. However, the current body of literature lacks statistical validation of this superiority. This study addresses this gap by employing five ML algorithms on 200 open-access datasets from a wide range of research contexts to statistically confirm the superiority of tree-based ML algorithms over their counterparts. Specifically, it examines two tree-based ML (Decision tree and Random forest) and three non-tree-based ML (Support vector machine, Logistic regression and k-nearest neighbour) algorithms. Results from paired-sample t-tests show that both tree-based ML algorithms reveal better performance than each non-tree-based ML algorithm for the four ML performance measures (accuracy, precision, recall and F1 score) considered in this study, each at p

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0301541

DOI: 10.1371/journal.pone.0301541

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