Do I need to install anything?

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

For Researchers · Naive Bayes

Naive Bayes for a sorting algorithm

Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a sorting algorithm 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 researchers?: Yes — this version of "Naive Bayes for a sorting algorithm" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.