What is AUC in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: AUC

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Area Under the Curve Rank-based measure of the model performance or the predictive power calculated as the area under the Receiver Operating Characteristic ROC curve.

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• Key Concepts: 
  AUC stands for Area Under the Curve. It is a metric used to measure the accuracy of a predictive model. It is calculated by plotting the true positive rate (TPR) against the false positive rate (FPR) and then calculating the area under the curve. AUC is a useful metric for evaluating binary classification models, as it provides an overall measure of accuracy. 
  
  How to use it: 
  In SAP Predictive Analytics, AUC can be used to evaluate the performance of a predictive model. To calculate AUC, you need to first generate a confusion matrix, which contains the true positive rate (TPR) and false positive rate (FPR). Then, plot the TPR against the FPR and calculate the area under the curve. The higher the AUC value, the better the model’s performance. 
  
  Tips & Tricks: 
  When evaluating a predictive model using AUC, it is important to remember that a higher AUC value does not necessarily mean that the model is better. It is important to consider other metrics such as precision and recall when evaluating a model’s performance. 
  
  Related Information: 
  For more information on AUC and how to use it in SAP Predictive Analytics, please refer to SAP’s documentation on predictive analytics.