What is 'ROC' in SAP BI-RA-PA - SAP Predictive Analytics?

ROC - Overview

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Receiver Operating Characteristic Graph derived from the signal detection theory. It portrays how well a model discriminates in terms of the tradeoff between sensitivity and specificity, or, in effect, between correct and mistaken detection as the detection threshold is varied.

ROC - Details

• 
  Key Concepts: 
  ROC stands for Receiver Operating Characteristic. It is a graphical representation of the performance of a predictive model. It is used to evaluate the accuracy of a model by plotting the true positive rate against the false positive rate. The area under the curve (AUC) is used to measure the accuracy of the model. 
  
  How to use it: 
  ROC can be used to compare different models and determine which one is more accurate. It can also be used to determine the optimal threshold for a given model. The optimal threshold is the point at which the true positive rate and false positive rate are equal. 
  
  Tips & Tricks: 
  When using ROC, it is important to remember that a higher AUC does not necessarily mean that the model is more accurate. The AUC should be interpreted in context with other metrics such as precision and recall. 
  
  Related Information: 
  ROC is closely related to precision-recall curves, which are used to evaluate models in imbalanced datasets. It is also related to lift curves, which are used to measure how much better a model performs than random guessing.