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.
List of documents	A compilation of text-based documents that encompass articles, reviews, and written material for training or evaluating NLP models.	Documents can be in formats like plain text, CSV, JSON, PDF, docx, or zip
Sentiment Type	Valence-based sentiment analysis aims to determine the overall opinion or feeling expressed in a piece of text using the intrinsic attractiveness (positive valence) or averseness (negative valence) of an event, object, or situation, without delving into the specific emotions involved. Emotion-based analysis can provide a more nuanced understanding of the sentiment by identifying the particular emotional state of the author such as happiness, sadness, anger, fear, surprise, and disgust.	valence / emotion
Compute Metrics	Compute the metrics for valence	yes / no
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.
Dropout	DROPOUT	Dropout percentage in deep neural networks. It is a regularization method used for better generalization over new data. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much and enhances their isolated learning.	0 to 90
Batch Size	BATCH_SIZE	The number of data points provided to the model at once (in one batch) during training. The batch size impacts how quickly a model learns and the stability of the learning process. It is an important hyperparameter that should be well-tuned. For more details, please visit our Blog.	16 / 32 / 64 / 128