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Training Parameters And Accuracy Measures

Our platform provides the flexibility to adjust a set of training parameters. There are general training parameters and advanced training options that could influence the model predictions. The predictions are measured on the basis of a set of accuracy measures or metrics that are also discussed in this section.

Training Options

Once you have fulfilled all the feature group requirements for the use case, you can set the following general and advanced training configuration options to train your ML model:

Training Option Name	Description	Possible Values
Name	The name you would like to give to the model that is going to be trained. The system generates a default name depending upon the name of the project the model is a part of.	The name can be comprised of any alphanumeric character and the length can be anywhere from 5 to 60 characters.
Set Refresh Schedule (UTC)	Refresh schedule refers to the schedule when your dataset is set to be replaced by an updated copy of the particular dataset in context from your storage bucket location. This value to be entered is a CRON time string that describes the schedule in UTC time zone.	A string in CRON Format. If you're unfamiliar with Cron Syntax, Crontab Guru can help translate the syntax back into natural language.

Advanced Training Options

For Advanced Options, our AI engine will automatically set the optimum values. We recommend overriding these options only if you are familiar with deep learning. Overview of Advanced Options:

Training Option Name	API Configuration Name	Description	Possible Values
Test Split	TEST_SPLIT	Percentage of the dataset to use as test data. A range from 5% to 20% of the dataset as test data is recommended. The test data is used to estimate the accuracy and generalizing capability of the model on unseen (new) data.	Percentage of the dataset to use as test data. A range from 5% to 20% of the dataset as test data is recommended. The test data is used to estimate the accuracy and generalizing capability of the model on unseen (new) data.
Epochs	EPOCHS	Number of full passes over the data the model sees during training.	Integers specifying the desired number of epochs.
System Prompt	SYSTEM_PROMPT	System prompt describes the overall intended behavior, profile, and role of the finetuned model.	Text field containing the system prompt.