Abstract: Naïve Bayes classifier is a simple and effective classification method，but its attribute independence assumption makes it unable to express the dependence among attributes in the real world，and affects its classification performance.Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness.All of them improve the performance of Naïve Bayes at the expense（to a greater or lesser degree） of execution time and/or simplicity of the final model.In this paper a simple method for setting attribute weights for using with Naïve Bayes is presented.Experimental results show that Naïve Bayes with attribute weights rarely degrades the quality of the model compared to standard Naïve Bayes，and in many cases，improves it dramatically.