How To Fix RS_DME_SC002 - Scoring model has been trained successfully

SAP Error Message - Details

• Message type: E = Error

• Message class: RS_DME_SC - Messages for Scoring

• Message number: 002

• Message text: Scoring model has been trained successfully

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message RS_DME_SC002 - Scoring model has been trained successfully ?

  The SAP error message RS_DME_SC002 indicates that a scoring model has been trained successfully, but it may not be functioning as expected in the context of your application. This message is typically associated with the SAP Predictive Analytics or SAP Business Technology Platform (BTP) environments where machine learning models are utilized.
  Cause:
  
  Model Training Success: The message itself indicates that the model training process has completed without errors. However, it does not guarantee that the model will perform well or that it is correctly integrated into your application.
  Integration Issues: There may be issues with how the model is being called or utilized in your application, leading to unexpected behavior or results.
  Data Quality: The input data used for scoring may not be in the expected format or may contain inconsistencies, which can affect the model's performance.
  Configuration Problems: There could be misconfigurations in the scoring process or in the parameters used to invoke the model.
  
  Solution:
  
  Verify Model Integration: Ensure that the model is correctly integrated into your application. Check the API calls or the method used to invoke the scoring model.
  Check Input Data: Validate the input data being fed into the model. Ensure it matches the expected format and contains no missing or erroneous values.
  Review Model Parameters: Look into the parameters used during the scoring process. Ensure they align with what the model expects.
  Monitor Model Performance: After training, monitor the model's performance on a validation dataset to ensure it is making accurate predictions.
  Consult Documentation: Refer to the SAP documentation for specific guidelines on using scoring models, including any prerequisites or configuration settings that may be required.
  Logs and Debugging: Check logs for any additional error messages or warnings that may provide more context on the issue.
  
  Related Information:
  
  SAP Predictive Analytics Documentation: Review the official SAP documentation for predictive analytics to understand the model training and scoring processes.
  SAP Community: Engage with the SAP Community forums to see if others have encountered similar issues and to share solutions.
  Model Evaluation: Consider using model evaluation techniques to assess the model's performance, such as confusion matrices, ROC curves, or other relevant metrics.
  
  If the issue persists after following these steps, it may be beneficial to reach out to SAP support for further assistance.