Therefore when this actual value is close to 0 MAPE becomes undefined as you will receive either infinity or a division by zero error. This is because in order to calculate MAPE we need to be able to divide by the actual value. When actual values are at or close to 0, MAPE is not defined. The mathematical formula for calculating MAPE is: How to define MAPE when actual is 0 MAPE is a popular metric to use as the error value is easily interpreted and comparable across datasets. MAPE is the aggregated mean of these errors, which helps us understand the model performance over the whole dataset. MAPE (Mean Absolute Percentage Error) is the mean of all absolute percentage errors between the predicted and actual values.Ībsolute percentage error is a row-level error calculation where the non-negative difference between the prediction and the actual is divided by the actual value to return the error as a relative percentage. In this post, I explain why this happens and what to do when it does. MAPE (Mean Absolute Percentage Error) is a common regression machine learning metric, but when the actual values are close to 0 it becomes undefined.
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