wehub-resource-sync
24 lines
1.1 KiB
Markdown
24 lines
1.1 KiB
Markdown
# Why is the Naive Bayes Classifier naive?
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Let's start by taking a quick look at the Bayes' Theorem:
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In context of pattern classification, we can express it as
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If we use the Bayes Theorem in classification, our goal (or objective function) is to maximize the posterior probability
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Now, let's talk a bit more about the individual components. The priors are representing our expert (or any other prior) knowledge; in practice, the priors are often estimated via MLE (computed as class frequencies). The evidence term cancels because it is constant for all classes.
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Moving on to the "naive" part in the Naive Bayes Classifier: What makes it "naive" is that we compute the conditional probability (sometimes also called likelihoods) as the product of the individual probabilities for each feature:
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Since this assumption (the absolute independence of features) is probably never met in practice, it's the truly "naive" part in naive Bayes.
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