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

mean square error - Overview

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Square root of the mean of the quadratic errors. The Mean Square Error is the average of the squares of the differences between the predicted and actual values. It's a quality measure of an estimator. It is always positive, and values closer to zero are better.

mean square error - Details

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  Key Concepts: 
  Mean Square Error (MSE) is a statistical measure of how close a predicted value is to the actual value. It is calculated by taking the average of the squared differences between the predicted and actual values. MSE is used in SAP Predictive Analytics to measure the accuracy of a predictive model. 
  
  How to use it: 
  MSE can be used to evaluate the performance of a predictive model. The lower the MSE, the better the model is at predicting values. To calculate MSE, subtract each predicted value from its corresponding actual value, square the differences, and then take the average of all these squared differences. 
  
  Tips & Tricks: 
  When using MSE to evaluate a predictive model, it is important to remember that it only measures accuracy and not other aspects such as interpretability or complexity. Therefore, it should be used in conjunction with other metrics such as R-squared or AUC-ROC to get a more comprehensive view of a model’s performance. 
  
  Related Information: 
  MSE is closely related to other metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). RMSE is calculated by taking the square root of MSE while MAE is calculated by taking the average of the absolute differences between predicted and actual values.