Both are widely used and easily implemented in Python using sklearn Differences between MAE and MAPE.Both aren’t sensitive to outliers as they use the absolute difference.Given that MAPE is a further development of the MAE calculation, there are some key similarities between them. Mape = mean_absolute_percentage_error(actual, prediction) Mae = mean_absolute_error(actual, prediction) The syntax for using them is: from trics import mean_absolute_percentage_error, mean_absolute_error Now we have the differences for each observation we can calculate the MAE and MAPE like so: #MAEīoth MAE and MAPE are easily implemented in Python using the sklearn package. Let’s implement these steps in an example using a house price prediction model.įirst we will calculate the absolute and percentage differences: Predicted price Actual price Absolute difference Percentage difference 100,000 105,000 5,000 4.8% 120,000 118,000 2,000 1.7% 160,000 170,000 10,000 5.9% Calculate the mean of the absolute differences for MAE and the mean of the percentage differences for MAPE.Calculate the absolute difference and percentage difference between the actual and prediction for each observation.To calculate MAE and MAPE you need to take the following steps: Now that you have learned a little more about MAE and MAPE let’s look at an example of them in practice. You can see that both MAE and MAPE use the absolute difference between the actual and the predicted, but MAPE takes this difference and turns it into a relative percentage by diving by the actual and multiplying by 100. This difference can be seen more clearly when looking at the mathematical formulas for the two metrics: Therefore, the key difference is that MAPE is returned as a percentage instead of an absolute value, as with MAE. MAE (Mean Absolute Error) is the mean absolute difference between the actual and the predicted value, whilst MAPE (Mean Absolute Percentage Error) is the mean absolute percentage difference between the actual and the predicted value. In this post I will explain the similarities, differences, and hopefully help you find out which you should use. Both metrics are built on the same calculation, so it’s often confusing to know whether you should use one of the other. MAE and MAPE are machine learning metrics for regression models.
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