🟩 MSE Loss Visualizer

Those squares aren't decoration — their AREA is the squared error, literally.

📋 How To Use This Lab
  1. Drag Slope and Intercept — watch a real square grow at every point, sized exactly to (error)².
  2. Press ▶ Optimize — the line rotates toward best fit while every square visibly shrinks.
  3. Flip MSE ⇄ L1 — squares become simple line segments; switch to the "With Outlier" dataset to see MSE get dragged around while L1 barely budges.
  4. Try different Datasets from the dropdown to see how loss behaves on clean vs noisy vs outlier-heavy data.
ŷ = 0.00x + 0.00
MSE = 0.000
Each square's area = (true y − predicted y)²
MSE (Squares)
L1 (Lines)
🟩 SQUARYDrag the sliders and watch real squares appear — their area IS the loss, not just a metaphor.
🧑‍🏫 PROF. TORCHMSE = mean((y − ŷ)²) — literally the average area of every one of these squares.