MAPE and nMAE are not available on scikit-learn, so explaining the exact computation can be more useful than relying on nomenclature. Therefore when using MAPE as an objective function, the estimator prefers smaller values and can be biased towards negative errors.ĭifferent error measures can target different requirements depending on datasets. For the same estimated value and actual value of 80, the MAPE is 0.125. What this means is that for the same error, the error is higher when aᵗ fᵗ.įor example, for the actual value 100 and estimated value of 90, the MAPE is 0.10. MAPE puts a larger penalty on negative errors □.To compute MAPE, data points with the actual value zero need to be excluded to avoid a division by zero error.MAPE however comes with its share of drawbacks. So if removing the influence of outliers is important for your use case, then MDAPE would be best to use. Because of this, MAPE is much more sensitive to outliers than MDAPE. nMAE on the other hand can lose some of the detail because of the aggregation of errors done before the averaging. The difference between MDAPE and MAPE is that MDAPE returns the median value of all the errors, whereas MAPE returns the mean. ![]() MAPE is computed over every data point and averaged, and therefore captures more errors and outliers. The small difference in the way the error is computed can produce very different results, specially if used as an objective function.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |