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

SAP Term: error mean

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Mean of the difference between predicted and actual values that quantifies the precision of the model's estimations. It is used to determine how precisely the mean of the model's predicted values estimates the population mean.

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• Key Concepts: 
  Error mean is a measure of the average difference between the predicted values and the actual values in a dataset. It is used to evaluate the accuracy of a predictive model. In SAP Predictive Analytics, error mean is calculated using the Mean Squared Error (MSE) metric. 
  
  How to use it: 
  Error mean can be used to compare different predictive models and determine which one is more accurate. To calculate error mean, first calculate the MSE for each model. Then, take the average of all the MSEs to get the error mean. 
  
  Tips & Tricks: 
  When comparing different models, it is important to consider other metrics in addition to error mean. For example, you should also look at the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). These metrics can provide additional insight into how accurate a model is. 
  
  Related Information: 
  Error mean is closely related to other metrics such as RMSE and MAE. It is also related to other measures of accuracy such as precision and recall. Understanding these metrics can help you better evaluate predictive models and make more informed decisions about which one to use.