Enter your data
Example data table
| Row | Actual | Predicted | |A − P| |
|---|---|---|---|
| 1 | 10 | 11 | 1 |
| 2 | 12 | 11 | 1 |
| 3 | 9 | 10 | 1 |
| 4 | 15 | 14 | 1 |
| 5 | 13 | 13 | 0 |
| MAE | 0.8 | ||
Formula used
Mean Absolute Error measures average magnitude of prediction errors.
- N is the number of paired samples.
- Absolute value ensures positive error magnitudes.
- Lower MAE indicates closer predictions on average.
How to use this calculator
- Choose an input mode: lists, paired rows, or CSV upload.
- Enter values or upload a file, then set decimals.
- Optional: enable extra metrics for deeper comparison.
- Click Calculate MAE to view results above.
- Use Download CSV or Download PDF for reporting.
What MAE tells you about model accuracy
MAE summarizes the average absolute distance between actual and predicted values across N paired observations. Each sample contributes |A-P|, so the metric is easy to explain to stakeholders for most teams. Because the penalty is linear, MAE is less sensitive to single extreme misses than squared-error metrics.
Interpreting MAE in the units of your target
MAE is expressed in the same unit as the outcome. If you forecast temperature, MAE is in degrees; if you estimate demand, MAE is in units sold; if you price a portfolio, MAE is in currency. This supports operational thresholds, such as “keep MAE under 5 units” to protect inventory. Since MAE depends on scale, compare models on the same dataset, or compute a normalized MAE such as MAE divided by the mean actual value, or by the range (max-min) for a 0 to 1 style score.
Handling outliers and uneven error costs
MAE treats underestimates and overestimates symmetrically and weights each row equally. If costs are asymmetric, report MAE separately for positive and negative errors, or calculate segment MAE by region, product, or time window. For noisy datasets, inspect the largest absolute errors, validate the raw records, and recompute MAE after removing duplicates or obvious data entry faults.
Comparing MAE with MSE and RMSE
MAE measures typical error magnitude, while MSE and RMSE emphasize larger mistakes by squaring errors. If two models have similar MAE but different RMSE, the one with higher RMSE is producing occasional large misses. When reporting, pair MAE with a percentile view, such as the median and 90th-percentile absolute error, to show what typical users experience and what worst-case cases look like in practice. Reporting MAE alongside RMSE provides a balanced view of consistency versus tail risk.
Data preparation checks that protect MAE
MAE assumes correct alignment of pairs. Sort actual and predicted series by the same key, confirm equal lengths, and spot-check a handful of rows for shifted timestamps or mismatched horizons. Standardize decimal precision, handle missing values consistently, and verify column indexes when importing files. Compare against a simple baseline, such as predicting the historical mean, to ensure improvements are meaningful.
FAQs
1) What does MAE measure?
MAE is the average of absolute differences between actual and predicted values. It reports typical error size in the same unit as your target, making it easy to interpret and compare against operational tolerances.
2) How is MAE different from RMSE?
MAE grows linearly with error, while RMSE squares errors and therefore penalizes large misses more. Use MAE for typical performance and RMSE when occasional big errors are especially costly.
3) Can MAE be zero or negative?
MAE is never negative because it averages absolute values. It becomes zero only when every predicted value exactly matches its corresponding actual value.
4) How many data points should I use?
More paired samples produce a more stable MAE. As a practical minimum, aim for dozens of rows, and prefer hundreds or more when the data is noisy or highly seasonal.
5) Does the calculator support decimals and negatives?
Yes. Enter integers or decimals, including negative values, as long as actual and predicted lists have the same length. Non-numeric entries are flagged to prevent incorrect calculations.
6) How do CSV columns and headers work here?
Choose the delimiter, tick the header option if the first row is labels, then set 0-based column indexes for actual and predicted fields. The tool reads each row, validates numbers, and ignores rows with missing required columns.