Naive Bayes Classifier

The Naive Bayes Classifier is a probabilistic machine learning algorithm based on Bayes' theorem, which assumes independence between features. It calculates the probability of each class given the input features and selects the class with the highest probability. This model is particularly effective for large datasets and is commonly used in text classification tasks, such as spam detection and sentiment analysis. Despite its simplicity, it often performs surprisingly well in practice, especially when the independence assumption holds. Its efficiency and ease of implementation make it a popular choice among data scientists and machine learning practitioners.