Empirical Performance of CART, C5.0 and Random Forest Classification Algorithms for Decision Trees.

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dc.contributor.authorOrounla, Bissilimou Rachidatou
dc.contributor.authorSode, Akoeugnigan Idelphonse
dc.contributor.authorSALAKO, Kolawolé Valère
dc.contributor.authorGlèlè kakaï, Romain
dc.date.accessioned2026-06-02T16:06:57Z
dc.date.available2026-06-02T16:06:57Z
dc.date.issued2023
dc.description.abstractThis study compares the performance of CART, C5.0 and Random Forest (RF) algorithms. 25 continuous predictors and 25 factors were simulated using a population size of 10,000. Based on this data, sample data were generated by varying the number of predictors, the proportion of categorical versus continuous predictors and the sample size. The performance of the tree algorithms increases with sample size and the number of variables, but for RF, it is highly greater than the one of CART and C5.0. Irrespective of the algorithms, the performance decreases when there are more categorical variables than continuous variables.
dc.identifier.doi10.16929/ajas/2023.1399.274
dc.identifier.otherBECDB-13713
dc.identifier.urihttps://dspace.uac.bj/handle/123456789/11731
dc.language.isofr
dc.relation.ispartofAfrican Journal of Applied Statistics
dc.subjectaccuracy
dc.subjectcategorical variables
dc.subjectspecificity
dc.subjectnon-parametric modeling
dc.subjectsimulation.
dc.titleEmpirical Performance of CART, C5.0 and Random Forest Classification Algorithms for Decision Trees.
dc.typeArticle

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