NaiveBayes，J48和RandomTree用外行的话来说

I am difficulty understanding how both classifiers work under the hood. So far I have deduced NaiveBayes predicts an outcome by 'uncoupling' multiple pieces of evidence, and to treating each of piece of evidence as independent. But when compared to another classification algorithm like J48 or RandomTree, how exactly is each different from another? I am difficulty understanding how both classifi