COMPARISON THE LOGISTIC REGRESSION, NAIVE BAYES CLASSIFICATION, AND RANDOM FOREST

Sri Wahyuni Kalumbang

doi:10.22146/jmt.60380

COMPARISON THE LOGISTIC REGRESSION, NAIVE BAYES CLASSIFICATION, AND RANDOM FOREST

https://doi.org/10.22146/jmt.60380

Sri Wahyuni Kalumbang^(1*)

(1) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Classification analysis is a method that aims to group a number of observations or observations into certain classes based on features or independent variables from these observations. In Hasti et al. {\cite{Hasti}} it is stated that statistical researchers in 2003 competed various classification methods and various metrics to measure their goodness in line with the scientific activities carried out by the statisticians above.
Therefore, the researcher is interested in comparing 2 statistic methods and the now popular method, namely the random forest, using data sets taken from kaggle.com and using FPR and accuration to see the goodness of the model.
The results of this study indicate that the random forest method has better predictive results than the other two methods seen from its accuration.

Keywords

machine learning, classification, classical method, random forest, FPR.

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DOI: https://doi.org/10.22146/jmt.60380