How To Fix RSDME235 - Some model fields had untrained values; hence are treated as missing


RSDME235 - Overview

  • Message type: E = Error

  • Message class: RSDME -

  • Message number: 235

  • Message text: Some model fields had untrained values; hence are treated as missing

  • Show details Hide details
  • What causes this issue?

    Some model fields had values not found during the training. Prediction
    was performed by treating such values as missing.
    Choose <LS>Show Message</> to view which model fields were affected.

    The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

    System Response

    How to fix this error?

    Ignore this message if the given values were intentional. Otherwise,
    use the possible entries function (F4 help) to choose the valid value.

    Procedure for System Administrators


    Error message extract from SAP system. Copyright SAP SE.


Smart SAP Assistant

  • What is the cause and solution for SAP error message RSDME235 - Some model fields had untrained values; hence are treated as missing ?
    The SAP error message RSDME235 indicates that some fields in a model (typically in SAP BW or SAP HANA) contain values that have not been trained or recognized by the model. This often occurs in the context of predictive analytics or machine learning models where certain input values do not match the expected or trained values.
    
    Cause: Untrained Values: The model has not been trained on certain values present in the dataset. This can happen if new categories or values are introduced after the model was trained. Data Quality Issues: There may be inconsistencies or errors in the data, such as typos, incorrect formats, or missing values. Changes in Data Schema: If the underlying data structure has changed (e.g., new fields added, existing fields modified), the model may not recognize the new or altered values. Insufficient Training Data: The model may not have been trained on a sufficiently diverse dataset, leading to gaps in its

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