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🤖 Complete Guide to Machine Learning

Train a live classifier in 3D: watch a decision-boundary surface warp via gradient descent to separate two classes of points, and see how model complexity, noise and learning rate change the fit.

AI & Machine Learning3DAdvanced60 FPS

A polynomial logistic regression classifier learns, live, in front of you: a rippling 3D surface trains by gradient descent to separate two clusters of labelled points, showing exactly how supervised learning fits a decision boundary to data.

🔬 What It Demonstrates

The surface's height at any point is the model's prediction there; training nudges weights via gradient descent on the cross-entropy loss until the surface separates the orange points from the indigo ones.

🎮 How to Use

Pick a dataset shape, set model complexity and noise, then press Train and watch loss fall and accuracy climb as the boundary warps to fit the data.

💡 Did You Know?

Low polynomial degree underfits curved data (a flat boundary can't separate moons or circles), while very high degree can overfit noisy labels — the same trade-off every real ML model faces.

⚙ Under the hood

Train a live classifier in 3D: watch a decision-boundary surface warp via gradient descent to separate two classes of points, and see how model complexity, noise and learning rate change the fit.

machine learningartificial intelligenceclassificationgradient descentneural networksdata analysisThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install