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Accessing text extraction model evaluation reports


After you build the model, you can evaluate it by using various accuracy measures, such as F-score, precision, recall, and so on. You can view the model evaluation report in the application or you can download that report to your directory. You can also view the test results for each record.

By using in-depth model analysis, you can determine whether the model that you created produces the results that you expect and reaches your accuracy threshold. By viewing record-by-record test results, you can fine-tune the training data to make your model more accurate when you rebuild it.

  • To download the model evaluation report, perform the following actions:

    1. In the Model analysis step, after the model finishes building, click Download report.

    2. Save the Model Analysis Report archive file to a local directory.

    3. Unpack the archive file.

      The archive file contains the following .csv extension files:
      • test_CRF_ id_number – Contains all test records. For each test record, you can view the result that you predicted (manual outcome), the result that the model predicted (machine outcome), and whether these result match.
      • test_CRF_SCORE_SHEET_ id_number – Contains accuracy measures for each entity in the model, for example, the number of true positives, precision, recall, and F-score.
      • test_DATA_SHEET_ id_number – Contains all testing and training records.
  • To view the summary results in Prediction Studio:

    1. Click the Expand icon next to the model name.

    2. In the Category summary tab, view the number of true positives, precision, recall, and F-score results per each entity type.

    3. In the Test results tab, for each test record, view the result that you predicted (actual), the result that the model predicted (predicted), and whether these results match.

  • Building machine-learning text extraction models

    Use Pega Platform machine-learning capabilities to create text extraction models for named entity recognition.

  • 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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