How To Fix RSDME304 - No valid decision tree formed

RSDME304 - Overview

• Message type: E = Error

• Message class: RSDME -

• Message number: 304

• Message text: No valid decision tree formed

What causes this issue?

The data used for training does not contain sufficient or the required
information to form a decision tree. This could be due the following
reasons:
Model parameters like <ZK>Min. Leaf Cases </>or <ZK>Leaf Node Accuracy
</> are set to very high values
Specified model fields in the model have no strong relation with the
predictive model field values
The data has too many missing values

System Response

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

How to fix this error?

Change model parameter values and perform training.
If there are some fields in the data that could have an effect on the
prediction model field value, include these fields in the model
definition (if they are not already included). Perform training again.
If you have used pruning, deactivate it and re-train the model. The
resulting tree may be prone to over-fitting and hence may not be
accurate.
If you have tried all of the above and you still do not obtain a valid
decision tree, this implies that the data you are using is not fit for
decision tree training.

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 RSDME304 - No valid decision tree formed ?

  The SAP error message RSDME304 "No valid decision tree formed" typically occurs in the context of SAP BW (Business Warehouse) when working with decision trees in the context of predictive analytics or data mining. This error indicates that the system was unable to create a valid decision tree based on the data provided.
  Causes:
  
  Insufficient Data: The dataset used for training the decision tree may not have enough records or may lack variability, making it impossible to form a valid model.
  Data Quality Issues: Missing values, outliers, or incorrect data types can lead to issues in forming a decision tree.
  Inappropriate Parameters: The parameters set for the decision tree algorithm may not be suitable for the dataset, such as overly restrictive settings.
  Feature Selection: If the features selected for the decision tree do not provide enough information or are not relevant, the model may fail to form.
  Class Imbalance: If the target variable has a significant