How To Fix RSDME360 - Model field &1 has too many values. Ignored for training

SAP Error Message - Details

• Message type: E = Error

• Message class: RSDME -

• Message number: 360

• Message text: Model field &1 has too many values. Ignored for training

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message RSDME360 - Model field &1 has too many values. Ignored for training ?

  The SAP error message RSDME360 indicates that a model field has too many values, which leads to it being ignored during the training of a predictive model. This typically occurs in the context of SAP BW (Business Warehouse) or SAP HANA when working with predictive analytics or machine learning models.
  Cause:
  
  High Cardinality: The field in question has a very high number of unique values (cardinality). This can happen with fields like customer IDs, product IDs, or any other identifier that has a large number of distinct entries.
  Data Volume: The dataset being used for training may contain a large volume of data, leading to a situation where the number of unique values exceeds the threshold set by SAP for model training.
  Configuration Settings: There may be specific configuration settings in the SAP system that limit the number of unique values that can be processed for model training.
  
  Solution:
  
  
  Reduce Cardinality:
  
  Aggregation: Consider aggregating the values of the field to reduce the number of unique entries. For example, instead of using individual customer IDs, you could group them by customer segments or categories.
  Binning: If applicable, you can bin continuous values into ranges (e.g., age groups, income brackets) to reduce the number of unique values.
  
  
  
  Feature Selection:
  
  Evaluate whether the field is necessary for the model. If it does not significantly contribute to the predictive power, consider removing it from the training dataset.
  
  
  
  Adjust Configuration:
  
  Check the configuration settings in your SAP system related to model training. There may be parameters that can be adjusted to allow for a higher number of unique values.
  
  
  
  Data Sampling:
  
  If the dataset is too large, consider using a representative sample of the data for training, which may help in reducing the number of unique values.
  
  
  
  Consult Documentation:
  
  Review SAP documentation or community forums for any specific guidelines or best practices related to handling high cardinality fields in predictive modeling.
  
  
  
  Related Information:
  
  SAP BW/4HANA: If you are using SAP BW/4HANA, ensure that you are familiar with the latest features and limitations regarding predictive analytics.
  SAP Predictive Analytics: Familiarize yourself with the capabilities and constraints of the SAP Predictive Analytics tool, especially regarding data preparation and model training.
  Performance Considerations: High cardinality fields can lead to performance issues in model training, so it’s essential to manage them effectively.
  
  By addressing the high cardinality issue and optimizing your dataset, you should be able to resolve the RSDME360 error and successfully train your predictive model.