Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Students · Naive Bayes

Naive Bayes for a convolutional net

Built for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition. Simulate a convolutional net live below — adjust the inputs and watch it respond, right in your browser.

Naive Bayes ClassifierLive

classification by probability

Controls

Presets

Class 1 mean

Class 2 mean

Spread σ

Prior P(class 1)

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.

▶ Run in Python

Data Inspector

Decision boundary0.28

Class overlaplow (easy)

Governing equation

P (C ∣ x) \propto P (C) N (x; μ_{C}, 1), P (C_{1}) = 0.5, \hat{C} = ar g C max

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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Frequently asked questions

Is this good for students?: Yes — this version of "Naive Bayes for a convolutional net" is framed for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.