What is Kullback-Leibler divergence in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: Kullback-Leibler divergence

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Measure of the difference between the cluster profile and the population profile of the variables.

Smart SAP Assistant

• Key Concepts: 
  Kullback-Leibler divergence (KL divergence) is a measure of the difference between two probability distributions. It is used in the SAP Predictive Analytics component of the Business Intelligence and Reporting Analysis (BI-RA-PA) suite to measure the difference between two probability distributions. It is a non-symmetric measure of the difference between two probability distributions, which means that the KL divergence from one distribution to another is not necessarily equal to the KL divergence from the other distribution to the first. 
  
  How to use it: 
  KL divergence is used in SAP Predictive Analytics to measure the difference between two probability distributions. This can be used to compare different models and determine which one is more accurate. It can also be used to compare different datasets and determine which one is more representative of a given population. 
  
  Tips & Tricks: 
  When using KL divergence, it is important to remember that it is a non-symmetric measure of difference. This means that the KL divergence from one distribution to another is not necessarily equal to the KL divergence from the other distribution to the first. Additionally, it is important to remember that KL divergence measures only the difference between two probability distributions and does not take into account any other factors such as sample size or data quality. 
  
  Related Information: 
  KL divergence is closely related to other measures of difference such as Jensen-Shannon divergence and Hellinger distance. Additionally, it can be used in conjunction with other techniques such as clustering and classification algorithms in order to improve accuracy and performance.