communications-mining
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- Introduction
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create or delete a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend dataset settings
- Delete messages via the UI
- Delete a dataset
- Export a dataset
- Using Exchange Integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Analytics vs. automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation (NEW)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Understanding data requirements
- 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
Important :
Communications Mining is now part of UiPath IXP. Check the User Guide Introduction for more details.
Communications Mining User Guide
Last updated Mar 25, 2025
- General model training
- What is the objective of training a model?
- Why can I not see anything in Discover if I've just uploaded data into the platform?
- How much historical data do I need to train a model?
- Do I need to save my model every time I make a change?
- How do I know what the performance of the model is?
- Why are there only 30 clusters available and can we set them individually?
- How many messages are in each cluster?
- What do precision and recall mean?
- Can I return to an earlier version of my model?
- Label training
- Can I change the name of a label later on?
- How do I find out the number of messages I have annotated?
- One of my labels is performing poorly, what can I do to improve it?
- What does the red dial next to my label or general field indicate? How do I get rid of it?
- Should I avoid annotating empty/uninformative messages?
