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Naive Bayes algorithm is generally not sensitive to zero values in the features under the assumption of feature independence. In traditional Naive Bayes classification algorithms, such as Gaussian Naive Bayes or Multinomial Naive Bayes, the independence assumption among features can help mitigate the impact of zero values. The independence assumption allows the algorithm to calculate probabilities without directly modeling the joint distribution of features. While Naive Bayes algorithms are designed to handle the independence assumption and are generally not sensitive to zero values, some care may be needed in specific scenarios, and techniques like Laplace smoothing can be applied to improve robustness, especially when dealing with count data.

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