What is mean absolute percentage error in SAP CA-ML-DAR - ?

SAP Term: mean absolute percentage error

• Component: CA-ML-DAR

• Component Name:

• Description: Data Attribute Recommendation Regression metric. Average of the absolute percentage errors of the the predictions. The lower the better. For predictions which are too high there is no upper limit to the percentage error, resulting in values that may exceed 100%.

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• Key Concepts: 
  Mean Absolute Percentage Error (MAPE) is a metric used to measure the accuracy of a model’s predictions. It is calculated by taking the average of the absolute percentage errors of all predictions made by the model. The MAPE is a measure of how close the model’s predictions are to the actual values. 
  
  How to use it: 
  The MAPE can be used to evaluate the performance of a model and compare it to other models. It can also be used to identify areas where the model needs improvement. To calculate the MAPE, first calculate the absolute percentage error for each prediction made by the model. Then, take the average of all these errors. The lower the MAPE, the more accurate the model’s predictions are. 
  
  Tips & Tricks: 
  When using MAPE to evaluate a model, it is important to remember that it is not always an accurate measure of accuracy. For example, if a model predicts a value that is very close to the actual value, but not exactly correct, then the MAPE will still be high. Additionally, if a model predicts values that are consistently too high or too low, then the MAPE will be high even though the predictions are consistent. 
  
  Related Information: 
  The MAPE is related to other metrics such as root mean squared error (RMSE) and mean absolute error (MAE). These metrics measure different aspects of accuracy and can be used in combination with each other to get a more complete picture of how accurate a model is. Additionally, there are other metrics such as precision and recall that can be used to evaluate models in different ways.