Naive Bayes Classifier

The Naive Bayes classifier is a simple yet effective probabilistic machine learning algorithm used for classification tasks, primarily aiming to predict class membership probabilities based on feature values. Current research focuses on improving its performance by addressing limitations like the strong conditional independence assumption through techniques such as weighted variable selection, optimal feature projections, and integrating it with other methods like ensemble learning and neural networks. These advancements enhance the classifier's accuracy, robustness, and interpretability, impacting various fields including text summarization, knowledge graph reasoning, and anomaly detection.