What is node pairing in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: node pairing

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Technique that allows identification of an item customer, product present in two different graphs thus with two different nodes, by means of its common neighbors. This technique can be used by a telephone company, for example, to detect "rotational churn" locate clients who change their phone susbcription on a regular basis to benefit from welcome offers.

Smart SAP Assistant

• Key Concepts: 
  Node pairing is a feature of SAP Predictive Analytics that allows users to create predictive models by pairing nodes in a graph. Node pairing is used to create relationships between different data points, allowing users to identify patterns and trends in their data. It is a powerful tool for creating predictive models that can be used to make decisions and predictions about future outcomes.
  
  How to use it: 
  Node pairing is used to create relationships between different data points in a graph. To use node pairing, users must first select the nodes they want to pair. Then, they must select the type of relationship they want to create between the nodes. This can be done by selecting the type of relationship from the drop-down menu or by manually entering the relationship type. Once the relationship type has been selected, users can then adjust the strength of the relationship by adjusting the weight of the connection between the nodes. 
  
  Tips & Tricks: 
  When using node pairing, it is important to consider how strong the relationship should be between two nodes. If the relationship is too weak, it may not be able to accurately predict future outcomes. On the other hand, if the relationship is too strong, it may lead to inaccurate predictions. It is important to find a balance between these two extremes when creating predictive models with node pairing. 
  
  Related Information: 
  Node pairing is just one of many features available in SAP Predictive Analytics. Other features include data mining, machine learning, and natural language processing. All of these features can be used together to create powerful predictive models that can help businesses make better decisions and predictions about future outcomes.