Regression Metrics Explorer 2D — MAE, RMSE, R² & MAPE

Model evaluation for a regression problem always comes down to comparing predicted values against actual ones, then compressing every point's error into a single headline number — but MAE, RMSE, R² and MAPE compress that same set of residuals in genuinely different ways. This 2D simulator renders a live scatter of a synthetic predicted-vs-actual dataset in the top panel (teal dots for true values, amber dots for the model's predictions, red connectors sized to each point's residual) plus a per-point error decomposition bar chart underneath. Dragging in noise spreads every prediction randomly and moves MAE and RMSE together, while dragging in outlier strength concentrates error into a single point and pulls RMSE sharply ahead of MAE — the exact mechanism behind "RMSE is outlier-sensitive, MAE is outlier-robust." A metric-focus switch recolors the bars by each metric's own per-point weighting so the difference is visible, not just numeric, and four live readouts (MAE, RMSE, R², MAPE) plus the RMSE/MAE ratio update every frame. Drag to pan the scatter and scroll/pinch to zoom when more points are on screen than fit at once.