How To Fix RSDME314 - Training data is insufficient to conduct training

SAP Error Message - Details

• Message type: E = Error

• Message class: RSDME -

• Message number: 314

• Message text: Training data is insufficient to conduct training

What causes this issue?

The mining source specified for training has either no records or the
number of records is less than the minimum leaf size (as specified in
the model parameters).

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?

If you have specified a larger minimum leaf case, reduce the number
sufficiently to match the number of records in the mining source.

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 RSDME314 - Training data is insufficient to conduct training ?

  The SAP error message RSDME314, which states "Training data is insufficient to conduct training," typically occurs in the context of SAP BW (Business Warehouse) or SAP BW/4HANA when you are trying to execute a data mining or predictive analysis model. This error indicates that the dataset you are using to train your model does not contain enough data points or relevant features to perform the training process effectively.
  Causes:
  
  Insufficient Data Volume: The dataset may not have enough records. Many algorithms require a minimum number of instances to learn effectively.
  Lack of Relevant Features: The dataset may not contain enough relevant attributes or features that are necessary for the model to learn from.
  Data Quality Issues: The data may contain too many missing values, duplicates, or irrelevant information, which can hinder the training process.
  Incorrect Data Selection: The data selection criteria may be too restrictive, leading to a very small subset of data being used for training.
  Data Segmentation: If the data is segmented into too many categories or classes, it may lead to insufficient data in each category for training.
  
  Solutions:
  
  Increase Data Volume: Ensure that you have a larger dataset. This may involve collecting more data or combining multiple datasets.
  Feature Engineering: Review the features in your dataset and consider adding more relevant features or transforming existing ones to improve the model's ability to learn.
  Data Cleaning: Clean the dataset to remove duplicates, handle missing values, and ensure that the data is relevant and of high quality.
  Adjust Data Selection Criteria: Review and possibly broaden the criteria used to select the training data to ensure that you are not filtering out too much data.
  Use Sampling Techniques: If the dataset is too large, consider using sampling techniques to create a balanced and representative training set.
  Review Model Requirements: Check the specific requirements of the algorithm you are using to ensure that your dataset meets those needs.
  
  Related Information:
  
  Documentation: Refer to SAP's official documentation for data mining and predictive analytics to understand the requirements for training data.
  SAP Community: Engage with the SAP Community forums to see if others have encountered similar issues and what solutions they have found.
  Data Profiling Tools: Use data profiling tools to analyze the dataset and identify issues related to data quality and volume.
  Model Evaluation: After addressing the data issues, evaluate the model's performance using appropriate metrics to ensure that it is learning effectively.
  
  By addressing the underlying causes of the error, you can improve the training process and successfully build your predictive model in SAP.