What is Partition strategy in SAP BI-RA-PA - SAP Predictive Analytics?

SAP Term: Partition strategy

• Component: BI-RA-PA

• Component Name: SAP Predictive Analytics

• Description: Technique allowing the decomposition of a dataset into two or three distinct sub-sets: a training dataset, a validation dataset and a testing dataset. This partition allows for cross-validation of the models generated.

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• Key Concepts: 
  Partition strategy is a feature of SAP Predictive Analytics that allows users to divide their data into smaller, more manageable chunks. This helps to reduce the amount of time and resources needed to process the data, as well as improve the accuracy of the results. The partition strategy also helps to reduce the risk of overfitting, which is when a model is too closely tailored to a specific dataset and does not generalize well to other datasets. 
  
  How to use it: 
  To use the partition strategy, users must first select the data they want to partition. This can be done by selecting a range of values or by using a specific algorithm. Once the data has been selected, users can then choose how they want to partition it. This can be done by selecting a number of partitions or by using a specific algorithm. Once the partitions have been created, users can then apply different algorithms or models to each partition in order to get more accurate results. 
  
  Tips & Tricks: 
  When using the partition strategy, it is important to ensure that the partitions are not too small or too large. If the partitions are too small, then there may not be enough data for the model to accurately predict results. On the other hand, if the partitions are too large, then there may be too much data for the model to accurately predict results. It is also important to ensure that each partition contains similar data so that the results are more accurate. 
  
  Related Information: 
  The partition strategy is closely related to other features of SAP Predictive Analytics such as feature selection and feature engineering. Feature selection is used to identify which features are most important for predicting results while feature engineering is used to create new features from existing ones. Both of these techniques can help improve the accuracy of predictive models when used in conjunction with the partition strategy.