Skip to main content


         This documentation site is for previous versions. Visit our new documentation site for current releases.      
 

This content has been archived and is no longer being updated.

Links may not function; however, this content may be relevant to outdated versions of the product.

Defining training and testing samples, and building the intent detection model

Updated on March 11, 2021

In the Sample construction step, determine which data to use to train the model and which data to use to test the model's accuracy.

During the training process of a text extraction model, the Maximum Entropy algorithm is applied on the training data, and the model learns to predict labels. The data that you designate for testing is not used to train the model. Instead, Pega Platform uses this data to compare whether the labels that you defined (for example, Complain, Purchase, and so on) match the labels that the model predicted.
  1. If you want to keep the split between the training and testing data as defined in the file that you uploaded, in the Construct training and test sets using section, select User-defined sampling based on "Type" column.
  2. If you want to ignore the split that is defined in the file and customize that split according to your business needs, perform the following actions:
    1. In the Construct training and test sets using section, select Uniform sampling.
    2. In the Training set field, specify the percentage of records that is randomly assigned to the training sample.
  3. Click Next.
  4. In the Model creation step, make sure that the Maximum Entropy check box is selected.
  5. Click Next.
    Result: The model training and testing process starts.
  • Previous topic Uploading data for training and testing of the intent detection model
  • Next topic Accessing intent analysis model evaluation reports

Have a question? Get answers now.

Visit the Support Center to ask questions, engage in discussions, share ideas, and help others.

Did you find this content helpful?

Want to help us improve this content?

We'd prefer it if you saw us at our best.

Pega.com is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us