How To Fix RS_DME_SC032 - Cancelation due to outliers in model field &1: &2

SAP Error Message - Details

• Message type: E = Error

• Message class: RS_DME_SC - Messages for Scoring

• Message number: 032

• Message text: Cancelation due to outliers in model field &1: &2

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message RS_DME_SC032 - Cancelation due to outliers in model field &1: &2 ?

  The SAP error message RS_DME_SC032 indicates that a cancellation occurred due to outliers detected in a model field. This typically arises in the context of data mining or predictive analytics within SAP systems, particularly when using the Data Mining Extension (DME) or similar functionalities.
  Cause:
  The error is triggered when the data being processed contains values that are considered outliers. Outliers are data points that deviate significantly from the rest of the dataset and can skew the results of statistical analyses or predictive models. The specific message provides two parameters:
  
  &1: The name of the model field where the outlier was detected.
  &2: The specific outlier value that caused the cancellation.
  
  Solution:
  To resolve this error, you can take the following steps:
  
  
  Identify Outliers: Review the data in the specified model field to identify the outlier values. You can use statistical methods (like Z-scores, IQR, etc.) to detect outliers.
  
  
  Data Cleaning: Depending on your analysis requirements, you may choose to:
  
  Remove the outlier values from the dataset.
  Transform the outlier values (e.g., capping or winsorizing).
  Investigate the cause of the outliers to determine if they are valid data points or errors.
  
  
  
  Adjust Model Parameters: If applicable, adjust the parameters of the model to be more robust against outliers. Some algorithms have options to handle outliers better.
  
  
  Re-run the Model: After addressing the outliers, re-run the data mining model to see if the error persists.
  
  
  Consult Documentation: Check SAP documentation or support resources for specific guidance related to the version of SAP you are using, as there may be additional settings or configurations that can help manage outliers.
  
  
  Related Information:
  
  Data Preprocessing: It is essential to preprocess your data adequately before running models. This includes handling missing values, normalizing data, and addressing outliers.
  Model Selection: Some models are more sensitive to outliers than others. Consider using robust algorithms if your data frequently contains outliers.
  SAP Notes and Support: If the issue persists, consider searching for SAP Notes related to RS_DME_SC032 or reaching out to SAP support for further assistance.
  
  By following these steps, you should be able to address the RS_DME_SC032 error and improve the robustness of your data mining processes in SAP.