> For the complete documentation index, see [llms.txt](https://docs.datamynd.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datamynd.ai/other/performance-and-accuracy-tuning.md).

# Performance and Accuracy Tuning

* 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 <support@datamynd.io> if you need help.  We have found ideal values between 300 and 2000 depending on the dataset (which can take some time to train)