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Random Forest: Random Forest uses a set of decision
trees selected randomly from the bootstrap sample of the
training dataset. The vote is aggregated from all the decision
trees, and the final score of classification is given. The
classifier is efficient with a big dataset and can handle a
large number of input variables. Moreover, it avoids overfitting
and improves generalizations. During the forest building, it
produces the estimate of unbiased generalization of error. The
best experimental parameters of the classifier are: There were
120 estimators with minimum sample leaves as 1, and 120
estimators with minimum sample split as 3 were used for best
classification accuracy. The Gini criterion is used to measure
the quality of the split
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IEEE/ICACT20220078 Slide.14
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