Absrtact: Naive Bayesian classifier is a simple and effective classifier, but its conditional independence assumption makes it unable to express the dependence among features.

摘要：朴素贝叶斯分类器是一种简单而高效的分类器，但它的条件独立性假设使其无法表示属性问的依赖关系。

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If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.

倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。

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Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.

朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。

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