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I understand that 'Naive' Bayes refers to the approach where all the features are assumed to be independent. But I want to evaluate the performance of each feature individually before I combine all of them and use them for prediction.

Can I still use sklearn's implementation of Gaussian Naive Bayes for making predictions using a single feature for this?

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I understand that 'Naive' Bayes refers to the approach where all the features are assumed to be independent.

Features are assumed to be conditionally independent given class label. And, you can fit Gaussian (or any other) Naive Bayes using one feature, in which it's not forced to be Naive any more.

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