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Creating machine-learning topic detection models
Efficiently connect your customers with the right consultant by providing training data to a topic detection model.
The machine learning topic detection model teaches itself based on training data provided to it, and then starts analyzing text on its own. The training data contains sample messages from customers with an assigned topic category. For example, the model assigns the message I want to book a ticket from New York to Warsaw to the Booking a flight category.
If you do not have enough training data for the machine learning model to start operating efficiently, consider creating a keyword-based topic detection model as a temporary substitute. For more information about the differences between topic detection model types, see Comparing keyword-based and machine-learning topic detection.
- Ensure that the system locale language settings are set to UTF-8.
- Specify a repository for text analytics models. For more information, see Specifying a database for Prediction Studio records.
- Setting up a machine-learning topic detection model
- Defining a taxonomy for machine learning topic detection
- Uploading data for training and testing of the topic detection model
- Defining the training and testing samples for topic detection
- Training and testing the topic detection model
- Reviewing the topic detection model
- Saving the topic detection model
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