Naïve Bayes
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May 1, 2024
4 minute read
Naïve Bayes is a simple yet powerful classification algorithm that is often used in machine learning. It is based on Bayes’ theorem, which provides a way to calculate the probability of an event occurring given the probability of its causes. Naïve Bayes makes the assumption that the features of an object are independent of each other, which is often not true in practice. However, despite this assumption, Naïve Bayes often performs well in practice and is a good choice for many classification problems.
How Naïve Bayes Works
To understand how Naïve Bayes works, let’s consider an example. Suppose we have a dataset of emails, and we want to classify each email as either spam or not spam. We can use Naïve Bayes to do this by following these steps:
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