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

SAP Term: mean absolute error

• Component: CA-ML-DAR

• Component Name:

• Description: Data Attribute Recommendation Regression metric. Average absolute difference between the predicted values and the actual values. The lower the better.

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• Key Concepts: 
  Mean Absolute Error (MAE) is a measure of the average magnitude of the errors in a set of predictions, without considering their direction. It measures the average magnitude of the errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight. 
  
  How to use it: 
  MAE is used to measure the accuracy of a model’s predictions. It is calculated by taking the absolute difference between each predicted value and its corresponding actual value, then taking the average of all these differences. The lower the MAE, the better the model’s accuracy. 
  
  Tips & Tricks: 
  When using MAE to evaluate a model, it is important to remember that it does not take into account the direction of the error. This means that if a model consistently over-predicts or under-predicts values, it will still have a low MAE score. 
  
  Related Information: 
  MAE is often used in conjunction with other metrics such as Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). These metrics are used to provide a more comprehensive view of a model’s accuracy. The Component CA-ML-DAR provides tools for measuring and evaluating these metrics.