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

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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.

    The model training and testing process starts.

  • Analyzing natural language

    Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.

  • Recognizing user intent

    Create intent analysis models to enable your application to detect the ideas that users express through written communication. For example, you can use an intent model when you want your chatbot to understand and respond when someone asks for help.

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