Naive Bayes classifies by asking which class most plausibly produced a data point. Watch the decision boundary move as the distributions and priors change.
Naive Bayes ClassifierLive
classification by probability
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Naive Bayes models each class as a probability distribution and assigns a new point to whichever class makes it most likely, weighted by the prior. Where the weighted curves cross is the decision boundary. It is fast, simple, and famously strong on text. Educational tool.
Reading this result: Well-separated classes with a balanced prior put the boundary right between the means, exactly where the two weighted curves cross.
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How it works
Naive Bayes models each class as a probability distribution and applies Bayes' theorem, assigning a point to the class with the highest posterior. Despite its naive independence assumption it is fast, robust, and remarkably effective for text and spam filtering. Educational tool.
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.