- 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
True and false positive and negative predictions
It’s important to understand these definitions as they form a key part of explaining other fundamental Machine Learning concepts like precision and recall.
The definitions below are outlined in the context of their application within the platform.
To start with:
- A ‘positive’ prediction is one where the model thinks that a label applies to a message
- A ‘negative’ prediction is one where the model thinks that a label does not apply to a message
True positives
A true positive result is one where the model correctly predicts that a label applies to a message.
True negatives
A true negative result is one where the model correctly predicts that a label does not apply to a message.
False positives
A false positive result is one where the model incorrectly predicts that a label applies to a message, when in fact it does not apply.
False negatives
A false negative result is one where the model incorrectly predicts that a label does not apply to a message, when in fact it does apply.
To understand each of these concepts in more detail, please see here.
