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Analyzing random forest classifier with different split measures
, M. Petare, P.K. Sinha
Published in Springer Verlag
Volume: 236
Pages: 691 - 699
Random forest is an ensemble supervised machine learning technique. The principle of ensemble suggests that to yield better accuracy, the base classifiers in the ensemble should be diverse and accurate. Random forest uses decision tree as base classifier. In this paper, we have done theoretical and empirical comparison of different split measures for induction of decision tree in Random forest and tested if there is any effect on the accuracy of Random forest. © Springer India 2014.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag
Open AccessNo