How To Fix RSDME953 - Invalid value for prediction parameter &1( &2 )

RSDME953 - Overview

• Message type: E = Error

• Message class: RSDME -

• Message number: 953

• Message text: Invalid value for prediction parameter &1( &2 )

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 RSDME953 - Invalid value for prediction parameter &1( &2 ) ?

  The SAP error message RSDME953, which states "Invalid value for prediction parameter &1 ( &2 )", typically occurs in the context of SAP BW (Business Warehouse) when working with predictive analytics or data mining functionalities. This error indicates that a parameter value provided for a prediction model is not valid or acceptable.
  
  Cause:
  
  
  Invalid Parameter Value: The parameter specified (denoted by &1) is not within the expected range or format. This could be due to:
  
  Typographical errors in the parameter value.
  Using a value that is not supported by the predictive model.
  Missing required parameters.
  
  
  
  Model 
  Configuration Issues: The predictive model may not be properly configured, leading to invalid parameter settings.
  
  
  Data Type Mismatch: The value provided may not match the expected data type for the parameter.
  
  
  Version Compatibility: There may be compatibility issues between the version of the predictive model and the data being used.
  
  
  
  Solution:
  
  
  Check Parameter Values: Review the values being passed for the prediction parameters. Ensure