- Getting Started
- Administration
- Manage Sources and Datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Preparing Data for .CSV Upload
- Model Training and Maintenance
- Understanding labels, entities and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Understanding the status of your dataset
- Model training and labelling best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to 'Refine'
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using 'Check label' and 'Missed label'
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using 'Rebalance'
- When to stop training your model
- Using Analytics & Monitoring
- Automations and Communications Mining
- FAQs and More
Training using 'Low confidence'
User permissions required: 'View Sources' AND 'Review and label'.
The final key step in Explore is training using 'Low confidence' mode, which shows you messages that are not well covered by informative label predictions. These messages will have either no predictions or very low confidence predictions for labels that the platform understands to be informative.
'Informative labels' are those labels that the platform understands to be useful as standalone labels, by looking at how frequently they're assigned with other labels.
This is a very important step for improving the overall coverage of your model. If you see messages which should have existing labels predicted for them, this is a sign that you need to complete more training for those labels. If you see relevant messages for which no current label is applicable, you may want to create new labels to capture them.
You can assign labels to messages in this mode in the same way as any other Explore mode.
To access this mode, use the dropdown in the top left-hand corner of the Explore page:
How much training should I do for this step?
This mode will present you with 20 messages at a time, and you should complete a reasonable amount of training in this mode, going through multiple pages of messages and applying the correct labels, to help increase the model's coverage (see here for a detailed explanation of coverage).
The total amount of training you need to complete in 'Low confidence' will depend on a few different factors:
- How much training you completed in Shuffle and Teach- the more training you do in Shuffle and Teach, the more your training set should be a representative sample of the dataset as a whole, and the fewer relevant messages there should be in 'Low confidence'
- The purpose of the dataset - if the dataset is intended to be used for automation and requires very high coverage, then you should complete a larger proportion of training in 'Low confidence' to identify the various edge cases for each label
At a minimum, you should aim to label five pages of messages in this mode. Later on in the Refine phase when you come to check your coverage, you may find that you need to complete more training in 'Low confidence' to improve your coverage further.
