Naive Bayes Visualizer - ML Learning Tool

📚 What is Naive Bayes?

Naive Bayes is a probabilistic classifier based on Bayes' Theorem. It assumes features are independent (the "naive" assumption) and calculates the probability of each class given the input features. Despite its simplicity, it's surprisingly effective for many real-world applications!

Bayes' Theorem: P(Class|Features) = P(Features|Class) × P(Class) / P(Features)

Real-world applications:

Email spam filtering (spam vs. not spam)
Sentiment analysis (positive vs. negative reviews)
Medical diagnosis (disease probability)
Document classification (news categorization)
Weather prediction

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📊 Sample Datasets:

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📊 Naive Bayes Classifier

📚 What is Naive Bayes?

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Training Data & Probability Map

Probability Distributions