Naive Bayes Algorithm

The Naive Bayes algorithm is a family of probabilistic algorithms based on Bayes' theorem, which assumes independence among predictors. It is particularly effective for classification tasks where the input features are categorical or text-based. The algorithm calculates the probability of each class given the input features and selects the class with the highest probability as the output. Common use cases include spam detection, sentiment analysis, and document classification. Its simplicity and efficiency make it a popular choice for many real-world applications in data science and machine learning.