Calculator
Example Data Table
| # | Actual | Predicted | Weight | Absolute Error |
|---|---|---|---|---|
| 1 | 10 | 11 | 1 | 1 |
| 2 | 12 | 11.5 | 1 | 0.5 |
| 3 | 9 | 8 | 2 | 1 |
| 4 | 15 | 14 | 1 | 1 |
| 5 | 13 | 13.2 | 1 | 0.2 |
Formula Used
How to Use This Calculator
- Select Manual lists or CSV upload.
- Enter Actual and Predicted values, or upload your CSV file.
- Optionally provide weights; otherwise a default weight is applied.
- Set rounding precision to match reporting requirements.
- Press Calculate to show results above the form.
- Use the download buttons to export CSV or a PDF summary.
Why MAE Matters for Regression Monitoring
Mean Absolute Error (MAE) expresses the average distance between predictions and targets in the same units as the label. That makes it easy to explain to stakeholders: “on average, we miss by 0.8°C” or “by 12 minutes.” Because MAE applies a linear penalty, it is less sensitive to rare spikes than squared-error metrics, so it often reflects typical user experience better.
Preparing Actual and Predicted Pairs
Reliable MAE starts with clean pairing. Each predicted value must correspond to the correct actual observation, after filtering, sorting, or time-windowing. If you evaluate a forecast, align by timestamp and horizon, not by row order. Standardize units, confirm any log or normalization transforms are reversed, and keep rounding rules consistent. This calculator skips invalid pairs, applies a default weight when needed, and reports how many rows were excluded.
Interpreting MAE with Baselines
MAE is most meaningful when compared to a baseline. A simple baseline might be predicting the training mean, the last observed value, or a seasonal average. Evaluate MAE on validation and test splits that match deployment conditions, then track changes over time. Small evaluation sets can fluctuate, so consider repeating runs or using resampling to estimate uncertainty. If MAE improves by 5–10% relative to baseline, the gain is often visible, but the acceptable threshold depends on domain tolerance and cost.
Using Weights to Reflect Business Cost
Not all errors carry equal impact. Weighted MAE lets you emphasize high-value customers, rare but critical events, or specific regions. Assign larger weights to observations with higher revenue, higher risk, or stricter service-level targets. The weighted formula scales each absolute error by its weight, then normalizes by total weight, so the metric stays comparable across datasets.
Reporting Diagnostics Beyond a Single Number
MAE alone can hide long-tail failures. Pair it with median absolute error for typical performance and the 95th percentile to understand worst-case behavior. Mean bias highlights systematic over‑prediction or under‑prediction. Review MAE by segment, device, or geography to detect localized drift. The per-row table helps audit individual outliers, while CSV and PDF exports support model cards, QA reviews, and drift investigations in production pipelines.
FAQs
1) What does MAE measure in a regression model?
MAE is the average absolute difference between actual and predicted values. It reports typical error magnitude in the target’s units, making it easy to interpret for business and engineering audiences.
2) How is MAE different from RMSE?
MAE uses absolute errors, while RMSE squares errors before averaging. RMSE penalizes large mistakes more strongly, so it is more sensitive to outliers. MAE usually better reflects typical deviation.
3) When should I use Weighted MAE?
Use it when certain samples are more important than others, such as high-revenue users, critical devices, or rare scenarios. Higher weights increase their influence on the overall metric.
4) Can I compare MAE across different datasets?
Yes, if the target variable and units are consistent. If scales change, MAE changes too. In those cases, compare relative improvements against a baseline or also report normalized metrics.
5) Why does the calculator show mean bias?
Bias is the average signed error (predicted minus actual). A positive value suggests systematic over‑prediction, while a negative value suggests under‑prediction. It helps diagnose calibration issues alongside MAE.
6) What is a good minimum sample size for MAE?
More is better, but start with at least a few dozen matched pairs for a quick check. For reporting, use hundreds or thousands when possible, and evaluate on a representative test set.