Naïve Bayes Classifier

Naïve Bayes Classifier is a machine learning algorithm used to categorize data based on probability. It assumes that features (characteristics of data) are independent, meaning each feature contributes to the outcome separately. By analyzing existing examples, it calculates the likelihood of data belonging to each category and picks the most probable one. Despite its simple assumption, it often works well for tasks like spam detection, sentiment analysis, and document classification, because it efficiently utilizes probabilities to make informed predictions even with limited data.