# The Naïve Bayes Algorithm

**Naïve Bayes** is a classification algorithm based on **Bayes' theorem** that *naïvely* assumes independence between features and assigns the same weight (degree of importance) to all features. This means that the algorithm assumes that no single feature correlates to or affects another. For example, although weight and height are somehow correlated when predicting a person's age, the algorithm assumes that each feature is independent. Additionally, the algorithm considers all features equally important. For instance, even though an education degree may influence the earnings of a person to a greater degree than the number of children the person has, the algorithm still considers both features equally important.

Note

Bayes' theorem is a mathematical formula that calculates conditional probabilities. To learn more about this theorem, visit the following URL: https://plato.stanford.edu/entries/bayes-theorem/.

Although real-life...