Analysis of error rate for various attributes to obtain the optimal decision tree

K. Mahesawri and S. Ramkumar

International Journal of Intelligent Enterprise, 2022, vol. 9, issue 4, 458-472

Abstract: The competitiveness and computational intelligence are required to increase the gross profit of the product in a market. The classification algorithm rpart is applied on retail market dataset. The regression rpart decision tree algorithm is implemented with principal component analysis to impute data in the missing part of the dataset. The objective is to obtain an optimal tree by analysing cross validation error, standard deviation error, and number of splits and relative error of various attributes. The results of various attributes by ANOVA method are compared to choose the best optimal tree. The tree with minimum error rate is considered for the optimal tree.

Keywords: decision tree; error rate; data mining and pruning. (search for similar items in EconPapers)
Date: 2022
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