Regression Metrics Calculator (MAE, MSE, RMSE, R²)
Paste predicted vs actual values and get MAE, MSE, RMSE, MAPE and R² — every regression error metric at once.
RMSE penalizes large errors more than MAE (it squares them), so RMSE ≫ MAE signals a few big misses. MAPE is scale-free but explodes near zero actuals. R² < 0 means your model is worse than predicting the mean.
Formula
About Regression Metrics Calculator (MAE, MSE, RMSE, R²)
Every regression model needs error metrics, but each tells a different story and reporting only one hides problems. This calculator takes your predicted and actual value pairs and computes the full set: MAE (average absolute miss, robust and interpretable), MSE/RMSE (outlier-sensitive, in the target's units), MAPE (percentage error, scale-free but unstable near zero) and R² (variance explained). Seeing them together is diagnostic — a large RMSE-to-MAE ratio reveals outliers, a negative R² reveals a model worse than the mean, and MAPE flags whether percentage thinking even applies.
How to use Regression Metrics Calculator (MAE, MSE, RMSE, R²)
- 1Enter your values into Regression Metrics Calculator (MAE, MSE, RMSE, R²) — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Regression Metrics Calculator (MAE, MSE, RMSE, R²)?
- ✓Computes Regression Metrics instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: MAE = mean|ŷ−y|.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
RMSE or MAE — which should I report?+
MAE if all errors are equally bad and you want an interpretable 'average miss'. RMSE if large errors are disproportionately costly (it squares them, so big misses dominate). Report both — their ratio is informative: RMSE ≈ MAE means uniform errors, RMSE ≫ MAE means a few large outliers are present.
What does a negative R² mean?+
Your model predicts worse than just always guessing the mean of the actuals. R² = 1 is perfect, 0 means you've matched the mean baseline, and negative means you're below it — usually a sign of a serious bug, a badly mismatched model, or evaluating on data very different from training.
Why is MAPE risky?+
It divides by actual values, so it blows up toward infinity as actuals approach zero and is undefined at zero. It also asymmetrically punishes over-prediction. For data spanning zero or with small values, prefer MAE, RMSE, or a symmetric/scaled alternative like sMAPE or MASE.
Are these metrics affected by the scale of my data?+
MAE, MSE and RMSE are in your target's units (or units²), so they're not comparable across different problems. MAPE and R² are scale-free and comparable. To compare models on the same data, any of them works; to compare across datasets, use MAPE or R² (with their caveats).
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