- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- 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
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields 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 annotating 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 general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Improving Balance and using Rebalance
User permissions required: 'View Sources' AND 'Review and annotate.
What is Balance and why is it important?
The Balance rating presented in the Model Rating in Validation is a reflection of how balanced the reviewed data (i.e. the training data) in a dataset is, when compared to the dataset as a whole.
It takes into account a number of contributing factors (as shown below), including:
- The similarity of the reviewed data to the unreviewed data (shown as a percentage score)
- The proportion of reviewed data that has been reviewed through random sampling (i.e. 'Shuffle' mode)
- The proportion of data that has been reviewed using 'Rebalance' (see below for detail)
- The proportion of data that has been reviewed whilst using 'Text search'
It's important that the proportion of data reviewed through random sampling is high (ideally 20%+) and the proportion of reviewed data annotated using search is low.
The balance rating is most heavily influenced, however, by the similarity score that measures the similarity of the unreviewed data to the reviewed data.
This similarity score is calculated by a proprietary annotating bias model that compares the reviewed and unreviewed data to ensure that the annotated data is representative of the whole dataset. If the data is not representative and has been annotated in a biased manner, model performance measures can be misleading and potentially unreliable.
Annotating bias in the platform is typically the result of an imbalance of the training modes used to assign labels, particularly if too much 'text search' is used and not enough 'Shuffle' mode. It can still occur, however, even if a high proportion of 'Shuffle' mode is used. Training specific labels in modes like 'Teach label' can naturally lead to a slight imbalance in the reviewed data. The platform helps you identify when this happens and helps you address it in a quick and effective way.
What is 'Rebalance' and how do you use it?
'Rebalance' is a training mode that helps to reduce the potential imbalances in how a model has been annotated, i.e. annotating bias, which mean that the reviewed data is a not as representative of the whole dataset as it could be.
The 'Rebalance' training mode shows messages that are underrepresented in the reviewed set.
Annotating the messages (as you would in any other training mode) presented in this mode will help address imbalances in the training data and improve the model's balance score.
Top Tip: Rebalance is typically most effective when used little and often. Annotating a small number of messages (between 10 and 20) in this mode and allowing the model to retrain before refreshing and annotating more examples is the best way to maximise the impact it will have on the model's balance score.
If you find that you have a high similarity score but the Balance rating is still low, this is likely because you have not annotated enough of the training data in 'Shuffle' mode. If this is the case, the platform will suggest annotating a random selection of messages as the priority recommended action. Training in this mode gives the platform additional confidence that the dataset has not been annotated in a biased manner and that the training data is a representative sample.
How much 'Rebalance' should I use?
You should continue to use 'Rebalance' iteratively to improve the similarity score for your model, which will in turn increase your 'Balance' rating.
Once this reaches a 'Good' rating in Validation, it is up to you how much more you would like to increase the similarity score before stopping training in 'Rebalance'.
You can aim to optimise this rating as much as possible, but continued training will always be a case of diminishing returns. A 'Good' rating should typically be considered an acceptable level of performance for a good model.
