Naive Bayes is a simple yet powerful probabilistic classification model used for various tasks, including spam detection, sentiment analysis, and text classification. It is based on the Bayes' theorem and the "naive" assumption that all features are independent of each other.
Key Concepts
Bayes' Theorem: It provides a way to calculate the probability of an event based on prior knowledge of conditions that might be related to the event.
Prior Probability: The probability of a class before any feature information is known.
Likelihood: The probability of observing the given features if the class is true.
Posterior Probability: The probability of the class given the features.
Assumptions
- All features are independent of each other.
- Each class has the same prior probability.
Types of Naive Bayes
- Gaussian Naive Bayes: Assumes that the features follow a Gaussian distribution.
- Multinomial Naive Bayes: Assumes that the features follow a multinomial distribution (common for text data).
- Bernoulli Naive Bayes: Assumes that the features follow a Bernoulli distribution (binary features).
Application
Naive Bayes is widely used in text classification tasks, such as:
- Spam Detection: Identifying whether an email is spam or not.
- Sentiment Analysis: Determining the sentiment of a piece of text (positive, negative, neutral).
- Document Categorization: Categorizing documents into predefined classes.
More Information
For further reading on Naive Bayes, check out our Introduction to Machine Learning.