A Naive Bayes classifier converges very quickly as compared to other models like logistic regression. As a result, we need less training data in case of naive Bayes classifier.
The main advantages of Naive Bayes classifier are:
- Simplicity and Ease of Implementation: Naive Bayes classifiers are simple and easy to implement. They are straightforward probabilistic classifiers based on Bayes’ theorem with the “naive” assumption of independence among features, making them computationally efficient and easy to understand.
- Efficiency in Training: Naive Bayes classifiers can be trained quickly even with a small amount of training data. This is because they have a simple probabilistic model that only requires estimating the probabilities of each class and the conditional probabilities of each feature given the class.
- Scalability: Naive Bayes classifiers scale well with the size of the dataset. They perform efficiently even with a large number of features and can handle high-dimensional data.
- Robustness to Irrelevant Features: Naive Bayes classifiers are robust to irrelevant features because they assume independence among features. This means that irrelevant features do not affect the classification decision as long as they are conditionally independent of the class given the class label.
- Suitability for Text Classification and Sparse Data: Naive Bayes classifiers are particularly well-suited for text classification tasks, such as spam filtering and document categorization, where the data is typically high-dimensional and sparse. They perform well even when the assumption of feature independence is not entirely met.
Overall, Naive Bayes classifiers are popular for their simplicity, efficiency, and effectiveness, particularly in scenarios where computational resources are limited or when dealing with high-dimensional data like text classification.