Author : R.SatyaRajendra Singh 1
Date of Publication :7th October 2016
Abstract: Random Forest algorithm is an one of the most essemble method for Classification analysis. Classification is a supervised learning approach, which maps a data items into predefined classes. There are various classification algorithms proposed in the literature. In this paper authors have used four classification algorithms such as J48, Random Forest (RF), Reduce Error Pruning (REP) and Logistic Model Tree (LMT) to classify the “CAR SALES NOMINAL” open source Data Set. Waikato Environment for Knowledge Analysis (WEKA) has been used in this paper for the experimental result and they found that Random Forest algorithm classify the given data set,it shows better results than the other algorithms for this specific data set. In this paper, the performance of classifier algorithms is evaluated for 5 fold cross validation test.finally ,Random Forest Algorithm have been proved as a benchmark classification method for getting maximum accuracy value and minimum error rate value.
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