Mean Absolute Percentage Error
Mean Absolute Percentage Error (MAPE) is a statistical metric used to measure the accuracy of forecasting or predictive models by calculating the average absolute percentage difference between predicted and actual values. It expresses errors as a percentage, making it easy to interpret across different scales and datasets. MAPE is commonly applied in fields like demand forecasting, financial modeling, and machine learning to evaluate model performance.
Developers should learn MAPE when building or evaluating regression models, especially in business contexts where percentage errors are more meaningful than absolute values, such as sales forecasting or inventory management. It is particularly useful for comparing models across datasets with varying magnitudes, as it normalizes errors, but caution is needed with zero or near-zero actual values to avoid division issues.