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Component: BI-RA-PA
Component Name: SAP Predictive Analytics
Description: Proportion of correct assignments to the class of non-signals – true negatives.
Key Concepts: Specificity is a measure of how accurately a predictive model can classify data points. It is calculated by dividing the number of true positives by the total number of positive predictions. A higher specificity indicates that the model is more accurate in predicting positive outcomes. How to use it: In SAP Predictive Analytics, specificity can be used to evaluate the performance of a predictive model. To calculate specificity, divide the number of true positives by the total number of positive predictions. A higher specificity indicates that the model is more accurate in predicting positive outcomes. Tips & Tricks: When evaluating a predictive model, it is important to consider both accuracy and specificity. A model with high accuracy but low specificity may be overfitting the data, while a model with low accuracy but high specificity may be underfitting the data. Related Information: Specificity is related to other measures of predictive performance such as precision and recall. Precision measures how many of the predicted positive outcomes are actually correct, while recall measures how many of the actual positive outcomes were correctly predicted.
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