What is 1-specificity in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: 1-specificity

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Proportion of false positives incurred out of all false positives on the validation data set.

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• Key Concepts: 
  1-specificity is a measure of how accurately a model can predict a certain outcome. It is calculated by dividing the number of true positives by the total number of positive predictions. A higher 1-specificity indicates that the model is more accurate in predicting the outcome. 
  
  How to use it: 
  In SAP Predictive Analytics, 1-specificity can be used to evaluate the accuracy of a predictive model. To calculate 1-specificity, divide the number of true positives (TP) by the total number of positive predictions (TP + FP). A higher 1-specificity indicates that the model is more accurate in predicting the outcome. 
  
  Tips & Tricks: 
  When evaluating a predictive model, it is important to consider both 1-specificity and 1-sensitivity. 1-sensitivity measures how many true positives were correctly identified, while 1-specificity measures how many false positives were correctly identified. Both measures should be taken into account when evaluating a predictive model. 
  
  Related Information: 
  1-specificity is related to other measures such as precision and recall. Precision measures how many true positives were correctly identified out of all positive predictions, while recall measures how many true positives were correctly identified out of all actual positives. Both precision and recall are important measures for evaluating predictive models.