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Training and testing the topic detection model

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Select the algorithms that Prediction Studio uses to build the model, and then start the building process.

  1. In the Model creation wizard step, select one or more algorithms to use for model creation:

    • Maximum Entropy
    • Naive Bayes
    • Support Vector Machine

    For more information about the available algorithms and their performance, see Training data size considerations for building text analytics models.

  2. Start the model creation process by clicking Next.

    The model creation process consists of the following stages:
    1. Initializing.
    2. Learning the taxonomy.
    3. Training the model based on the training sample.
    4. Testing the model against the testing sample.
After the model creation process finishes, review the model accuracy. For more information, see Reviewing 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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