Accuracy comparison between decision tree and naive Bayes algorithms in large discrete dataset with incompletely independent attributes
Hao Li, Yu Wan, Junbo Yang · 2023
Decision tree and naive bayes algorithms as classification algorithms are largely implemented in data mining. They are widely used in various data types which present accuracy differences among each type. In this essay, principles of two algorithms are introduced, and a specific data type, a large discrete dataset with incompletely independent attributes is used as a model to train those two algorithms. Then, two algorithms are tested, and confusion matrix method is used to calculate the accuracy of algorithms. Finally, two algorithms are compared, and it is concluded that accuracy of decision trees is better with large discrete datasets and attribute correlation.