How To Fix RSDME366 - Predictable model field &1 is CONTINUOUS. Specify binning intervals

SAP Error Message - Details

• Message type: E = Error

• Message class: RSDME -

• Message number: 366

• Message text: Predictable model field &1 is CONTINUOUS. Specify binning intervals

What causes this issue?

Content type of predictable model field is CONTINUOUS but no explicit
binning intervals are specified.

System Response

Content type of predictable model field should be DISCRETE. If the data
content is CONTINUOUS, then they have to be discretized by binning.

How to fix this error?

Specify binning intervals in the model field values

Procedure for System Administrators

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

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message RSDME366 - Predictable model field &1 is CONTINUOUS. Specify binning intervals ?

  The SAP error message RSDME366 indicates that you are trying to create or modify a predictive model in SAP BW (Business Warehouse) or SAP HANA, and the model field you are working with is defined as "CONTINUOUS." This means that the field is treated as a continuous variable, which typically requires binning (i.e., dividing the continuous range into discrete intervals) for certain types of predictive modeling.
  Cause:
  The error occurs because the predictive modeling algorithm you are using requires the continuous variable to be transformed into discrete bins. This is often necessary for algorithms that work better with categorical data or when you want to analyze the data in specific ranges.
  Solution:
  To resolve this error, you need to specify binning intervals for the continuous field. Here’s how you can do that:
  
  
  Define Binning Intervals:
  
  Go to the predictive model configuration where you have defined your fields.
  Locate the continuous field that is causing the error.
  Specify the binning intervals for this field. You can define the ranges manually or use predefined methods (like equal-width or equal-frequency binning).
  
  
  
  Use the Binning Functionality:
  
  In SAP BW or HANA, you may have options to create bins directly in the modeling interface. Look for options like "Create Bins" or "Define Binning" in the field properties.
  Set the lower and upper limits for each bin and define the bin size or number of bins as needed.
  
  
  
  Validate the Model:
  
  After defining the bins, validate the predictive model to ensure that the changes have been applied correctly and that there are no further errors.
  
  
  
  Re-run the Model:
  
  Once the binning is set up correctly, you can re-run the predictive model to see if the error has been resolved.
  
  
  
  Related Information:
  
  
  Binning Techniques: Common techniques for binning include:
  
  Equal-width Binning: Divides the range of the variable into equal-sized intervals.
  Equal-frequency Binning: Divides the data into bins such that each bin has approximately the same number of observations.
  Custom Binning: You can define specific ranges based on business logic or data distribution.
  
  
  
  Documentation: Refer to SAP documentation for predictive analytics and modeling for detailed instructions on how to set up binning and other preprocessing steps.
  
  
  Best Practices: When binning, consider the distribution of your data and the business context to ensure that the bins are meaningful and useful for analysis.
  
  
  By following these steps, you should be able to resolve the RSDME366 error and successfully create your predictive model.