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  1. SAP Glossary
  2. SAP Predictive Analytics
  3. mean absolute percentage error


What is mean absolute percentage error in SAP BI-RA-PA - SAP Predictive Analytics?


SAP Term: mean absolute percentage error

  • Component: BI-RA-PA

  • Component Name: SAP Predictive Analytics

  • Description: Average of the sum of the absolute values of the percentage errors. It measures the accuracy of the model's forecasts and indicates how much the forecasts differ from the real signal value.


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  • Key Concepts: 
    Mean Absolute Percentage Error (MAPE) is a measure of prediction accuracy used in SAP Predictive Analytics. It is calculated by taking the average of the absolute percentage errors of all predictions made. This measure is useful for comparing different models and determining which one is more accurate.
    
    How to use it: 
    MAPE can be used to evaluate the accuracy of a predictive model. To calculate MAPE, first calculate the absolute percentage error for each prediction made by the model. Then, take the average of all these errors to get the MAPE. The lower the MAPE, the more accurate the model is.
    
    Tips & Tricks: 
    When using MAPE to compare different models, it is important to make sure that all models are evaluated on the same data set. This will ensure that any differences in accuracy are due to differences in the models and not due to differences in the data sets.
    
    Related Information: 
    MAPE is just one measure of prediction accuracy. Other measures include root mean squared error (RMSE) and mean absolute error (MAE). Each measure has its own advantages and disadvantages, so it is important to choose the right one for your specific application.
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