How To Fix PAL_PRECOND_CHECK301 - Requirement class not OK

SAP Error Message - Details

• Message type: E = Error

• Message class: PAL_PRECOND_CHECK - PAL precondition check messages

• Message number: 301

• Message text: Requirement class not OK

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.

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Smart SAP Assistant

• What is the cause and solution for SAP error message PAL_PRECOND_CHECK301 - Requirement class not OK ?

  The SAP error message PAL_PRECOND_CHECK301: Requirement class not OK typically occurs in the context of the SAP Predictive Analytics Library (PAL) when certain preconditions for executing a specific algorithm or function are not met. This error is often related to the data or configuration settings required for the predictive model or analysis you are trying to run.
  Causes:
  
  Data Quality Issues: The input data may not meet the requirements of the algorithm. This could include missing values, incorrect data types, or insufficient data points.
  Configuration Errors: The parameters or settings for the algorithm may not be correctly configured. This includes issues with the model settings or the environment setup.
  Incompatible Data Types: The data types of the input variables may not be compatible with the requirements of the algorithm.
  Insufficient Data: The dataset may not have enough records or may not meet the minimum requirements for the algorithm to function properly.
  Missing Required Variables: Certain algorithms may require specific variables to be present in the dataset, and their absence can trigger this error.
  
  Solutions:
  
  Check Data Quality: Ensure that the input data is clean, with no missing values or incorrect data types. You can use data profiling tools to assess the quality of your data.
  Review Algorithm Requirements: Consult the documentation for the specific PAL algorithm you are using to understand its requirements and ensure that your data meets these criteria.
  Adjust Configuration Settings: Verify that all parameters and settings for the algorithm are correctly configured. This includes checking for any required parameters that may have been omitted.
  Validate Data Types: Ensure that the data types of the input variables match the expected types for the algorithm. For example, categorical variables should be properly encoded.
  Increase Dataset Size: If the dataset is too small, consider adding more data to meet the minimum requirements for the algorithm.
  Include Required Variables: Make sure that all necessary variables are included in the dataset. If certain variables are required by the algorithm, ensure they are present and correctly formatted.
  
  Related Information:
  
  SAP Documentation: Refer to the official SAP documentation for the Predictive Analytics Library to understand the specific requirements and limitations of the algorithms you are using.
  SAP Community: Engage with the SAP Community forums to seek advice from other users who may have encountered similar issues.
  Error Logs: Check the SAP system logs for additional error messages or warnings that may provide more context about the issue.
  Testing with Sample Data: If possible, test the algorithm with a known good dataset to confirm that the issue is with the data or configuration rather than the algorithm itself.
  
  By following these steps, you should be able to identify the cause of the error and implement a solution to resolve it.