Mean Absolute Percentage Error for regression models
نویسندگان
چکیده
منابع مشابه
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We show that universal consistency of Empirical Risk Minimization remains possible using the MAPE instead of the MAE.
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Research has suggested that the selection of an error measure has an important effect on the conclusions about which of a set of forecasting methods is most accurate (Armstrong and Collopy 1992; Fildes 1992). This research had concluded that the Relative Absolute Error (RAE) is a useful measure, especially when making comparisons across a small set of time series, where the time series differ s...
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When comparing how well different algorithms forecast time series, researchers use an average value of the ratio |x− y| (|x|+ |y|)/2 , known as the Symmetric Mean Absolute Percentage Error (SMAPE). In this paper, we provide a system-motivated explanation for this formula. We also explain how this formula explains the use of geometric mean to combine different forecasts.
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.12.114