A naive bayes classifier is a simple probabilistic classification algorithm based on applying Bayes' theorem with strong (naive) independence assumptions between the features. This method uses conditional probability to determine the likelihood of different classes, making it particularly effective in applications such as spam detection and text classification. The model assumes that the presence of a particular feature in a class is independent of the presence of any other feature, which simplifies calculations but may not always hold true in real-world data.
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