Both the Naïve Bayesian and the decision trees algorithms are classification algorithms. A Naïve Bayesian predictive model serves as a good benchmark for comparison to other models, while the decision trees algorithm is the most intuitive and widely applied algorithm. Which one has the best accuracy?
Performance metrics like accuracy are mostly dependent of type of data your algorithm is trying to learn and predict from. There is no standard like which algorithm does better than other as it mainly depends on the type of data and hyper parameters of an algorithm.
Performance metrics like accuracy are mostly dependent of type of data your algorithm is trying to learn and predict from. There is no standard like which algorithm does better than other as it mainly depends on the type of data and hyper parameters of an algorithm.
Performance metrics like accuracy are mostly dependent of type of data your algorithm is trying to learn and predict from. There is no standard like which algorithm does better than other as it mainly depends on the type of data and hyper parameters of an algorithm.
Here is one useful discussion on stack overflow.
https://stackoverflow.com/questions/10317885/decision-tree-vs-naive-bayes-classifier
Hope this helps