Naive Bayes
Naive Bayes is a probabilistic algorithm used in machine learning for classification tasks. It is based on Bayes' theorem, which states that the probability of a hypothesis (in this case, a class label) is proportional to the probability of the evidence (in this case, the features or attributes of the data) given that hypothesis. Naive Bayes assumes that all features are independent of each other, which is often not true in real-world data, but this simplification allows for efficient and effective classification. Naive Bayes is commonly used in text classification, spam filtering, and sentiment analysis.
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