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The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics. It usually expresses the accuracy as a ratio defined by the formula:
10 mai 2021 · MAPE is commonly used because it’s easy to interpret. For example, a MAPE value of 14% means that the average difference between the forecasted value and the actual value is 14%. The following example shows how to calculate and interpret a MAPE value for a given model.
The mean absolute percentage error (MAPE) — also called the mean absolute percentage deviation (MAPD) — measures accuracy of a forecast system. It measures this accuracy as a percentage, and can be calculated as the average absolute percent error for each time period minus actual values divided by actual values.
L’indicateur suivant permet en outre la comparaison entre séries d’écarts. L’erreur absolue moyenne en pourcentage ( Mean Absolute Percentage Error, alias MAPE ) : moyenne des écarts en valeur absolue par rapport aux valeurs observées. C’est donc un pourcentage et par conséquent un indicateur pratique de comparaison.
sklearn.metrics.mean_absolute_percentage_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average') [source] ¶. Mean absolute percentage error (MAPE) regression loss. Note here that the output is not a percentage in the range [0, 100] and a value of 100 does not mean 100% but 1e2.
Learn what MAPE is, how to calculate it, and how to use it to measure forecasting accuracy. Also, discover its limitations and how to monitor it for model performance.
5 juil. 2019 · The Mean Absolute Percentage Error (MAPE) is one of the most commonly used KPIs to measure forecast accuracy. MAPE is the sum of the individual absolute errors divided by the demand (each period separately). It is the average of the percentage errors. MAPE is a really strange forecast KPI.
