# Why does Naive Bayes work better when the number of features >> sample size compared to more sophisticated ML algorithms?

Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.

Why does Naive Bayes work well when the number of features >> sample size compared to more sophisticated ML algorithms?

If your data has $$k$$ dimensions, then a fully general ML algorithm which attempts to learn all possible correlations between these features has to deal with $$2^k$$ possible feature interactions, and therefore needs on the order of $$2^k$$ many data points to be performant. However because Naive Bayes assumes independence between features, it only needs on the order of $$k$$ many data points, exponentially fewer.