What is category significance in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: category significance

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Measure of the impact a category has on the target.

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• Key Concepts: 
  Category significance is a feature of SAP Predictive Analytics that allows users to identify the most important categories in a dataset. It is used to determine which categories are most influential in predicting the outcome of a model. Category significance is calculated by analyzing the correlation between each category and the target variable. 
  
  How to use it: 
  Category significance can be used to identify which categories are most important in predicting the outcome of a model. To use it, select the target variable and then select the categories that you want to analyze. The category significance will be displayed as a percentage, with higher percentages indicating more influence on the target variable. 
  
  Tips & Tricks: 
  When using category significance, it is important to remember that correlation does not always equal causation. Just because two variables are correlated does not mean that one causes the other. It is also important to consider other factors that may be influencing the results, such as outliers or missing data points. 
  
  Related Information: 
  Category significance is related to other features of SAP Predictive Analytics, such as feature importance and variable importance. Feature importance measures how much each feature contributes to the overall accuracy of a model, while variable importance measures how much each variable contributes to the overall accuracy of a model.