DataMynd Docs
Synthetic Data Generator Docs
Synthetic Data Generator Docs
  • Synthetic Data Generator
    • About
    • Requirements And Setup
    • Private Link Information
  • Application Workflow
    • Project Setup
    • Select ML Model
    • Data Config
    • Train and Generate
    • Quality Report and Preview
  • Other
    • Object Privileges
    • Performance and Accuracy Tuning
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Performance and Accuracy Tuning

Several notes and tips on tuning performance and accuracy for the app.

PreviousObject Privileges

Last updated 7 months ago

  • Categorical fields can cause the model training to take longer, along with the number of distinct values for each categorical field. Therefore, the user should be mindful of the number of categorical fields for each table in the model. We also recommend starting small with a smaller number of categorical fields before advancing to different models and more categorical fields / records. This restriction will be lifted in future versions of the app.

  • Training time may vary based on several factors including data complexity, number of fields (especially categorical fields), and amount of data.

  • •For neural net-based models, the # of training epochs will determine the accuracy of the model. An ideal value can require a fair bit of testing to determine. Feel free to send an email to if you need help. We have found ideal values between 300 and 2000 depending on the dataset (which can take some time to train)

support@datamynd.io