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Defining the training and testing samples for topic detection


Split the uploaded data into a set for training the model and a set for testing the model accuracy.

The topic detection model teaches itself based on the training data that you provide. Prediction Studio tests the model against the data that you mark for testing.

  1. In the Sample construction wizard step, specify how you want to split the training and testing samples by performing one of the following actions:

    • If you want Prediction Studio to test the model against the records for which you entered Test in the Type column, select User defined sampling. Use this option if you want to ensure accuracy by testing specific sentences against every model that you generate.
    • If you want to randomly assign records for testing, select Uniform sampling, and then manually specify the percentage of records that you want to test against.
  2. If the model creation wizard displays issues in the Warnings section, address the issues before proceeding.

    The issues displayed by the wizard refer to the training and testing sample that you provide. Example issues include:
    • Incorrectly formatted columns or missing values.
    • Categories from the taxonomy do not have a match in the training and testing sample.
    • Categories from the training and testing sample do not have a match in the taxonomy.
  3. Click Next.

Select the algorithms that Prediction Studio uses to build the model, and then start the building process. For more information, see Training and testing the topic detection model.

  • Creating machine-learning topic detection models

    Efficiently connect your customers with the right consultant by providing training data to a topic detection model.

  • Detecting the topics of text fragments

    Efficiently categorize and rout customer inquiries to the corresponding customer service consultant with topic detection. Topic detection scans a piece of text and determines the underlying topic, and then automatically assigns the text to a predefined category.

  • 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.

  • Text analytics accuracy measures

    Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.

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