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For Students · Classification Pipeline

Classification Pipeline for a dice-fairness check

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 dice-fairness check live below — adjust the inputs and watch it respond, right in your browser.

Principal Component AnalysisLive

Controls

Presets

PCA finds the directions along which data varies most. The eigenvectors of the covariance matrix are the principal components (cyan = most variance, green = least), and their eigenvalues are the variances along each. Projecting onto the top components is the basis of dimensionality reduction.

▶ Run in Python

Data Inspector

Variance PC10.00
Variance PC20.00
PC1 explains0%
PC1 angle

Governing equation

Reading this result: PC1 explains 0% of the variance here; the stronger the correlation between features, the more PCA concentrates information on that first component.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Neural Network PlaygroundLive

Controls

Presets

A tiny multilayer perceptron trained by real backpropagation. The background shows its learned decision boundary; dots are the training data, colored by true class.

▶ Run in Python

Data Inspector

Epoch0
Loss0.0000
Architecture2→8→1

Governing equation

Reading this result: With 8 hidden units the net bends the boundary into curves — more units capture finer shapes like the spiral, but too many can overfit.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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More with Classification Pipeline

Frequently asked questions

Is this good for students?
Yes — this version of "Classification Pipeline for a dice-fairness check" 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.