MAPE Calculator
Calculate mean absolute percentage error for regression forecast accuracy.
About MAPE Calculator
Mean Absolute Percentage Error (MAPE) expresses prediction error as a percentage of the actual value: MAPE = (100/n) × Σ|(y_i − ŷ_i)/y_i|. Because it's scale-independent, MAPE is popular for comparing forecast accuracy across series with very different magnitudes (e.g. comparing demand forecasts for a $10 product and a $10,000 product on the same scale). Its major limitation is that it's undefined when actual values are zero and becomes extremely large (and asymmetric) for actual values near zero, so it should be avoided when the target can be zero or close to it.
Formula
MAPE = (100 / n) * sum(|(y_i - y_hat_i) / y_i|)
- n
- Number of valid samples (y ≠ 0)
- y_i
- Actual value for sample i
- \hat{y}_i
- Predicted value for sample i
Worked example
With the default values below, MAPE Calculator returns MAPE of 7.29%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Actual Values
- 100, 200, 300, 400
- Predicted Values
- 110, 190, 320, 370
Results
- MAPE
- 7.29%
- Valid Samples (y ≠ 0)
- 4
Step by step
- 1Sum of absolute percentage errors: Σ|(y_i − ŷ_i)/y_i|= 0.2917
- 2MAPE: (100 / n) × sum of absolute percentage errors= 7.29%
Assumptions and limits
- The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why is MAPE undefined when actual values are zero?
MAPE divides by the actual value for each term, so a zero actual value causes division by zero; this calculator skips those data points and computes MAPE only over the remaining valid samples.
Why is MAPE considered asymmetric?
MAPE penalizes over-predictions and under-predictions differently in percentage terms — since the percentage error is capped at 100% for under-predictions (predicting 0 when actual is 100) but unbounded for over-predictions (predicting 1000 when actual is 100), it can bias model selection toward under-forecasting.
When should I use sMAPE instead of MAPE?
sMAPE (symmetric MAPE) is preferred when you want a percentage-based error metric that treats over- and under-predictions symmetrically, and it's better behaved when actual values are close to zero.