Naïve Bayes assumes

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 Great question 👍

Let’s analyze each statement:

  1. “Naïve Bayes assumes that features are conditionally independent given the class label.”
    ✅ Correct — this is the fundamental assumption of Naïve Bayes.

  2. “Gaussian Naïve Bayes is suitable for datasets with continuous features.”
    ✅ Correct — Gaussian NB models continuous features using a normal distribution.

  3. “Multinomial Naïve Bayes is commonly used for text classification tasks.”
    ✅ Correct — It works well with word counts / term frequencies in NLP.

  4. “Naïve Bayes always outperforms logistic regression on all datasets.”
    ❌ Incorrect — Performance depends on the dataset; logistic regression often outperforms NB when features are correlated.

✅ Answer: The incorrect statement is the last one:
“Naïve Bayes always outperforms logistic regression on all datasets.”

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