Welcome > Set up classification > Adaptive Feature Classifier > Enable auto optimization and improve classification

Enable auto optimization and improve classification

Use the Auto Optimization window to optimize the parameters for the Adaptive Feature Classifier. The best practice is to use a training set for auto optimization. This is because the Classification Set may not contain enough documents to provide successful optimization results.

In many cases, the default options are sufficient. If you want to modify these settings, the best practice is to generate a classification benchmark before optimization, and then another benchmark after each change. Recording all changes made along the way will ensure you can replicate your results.

You can enable auto optimization by following these steps:

  1. Open the Documents window if it is not already open.
  2. Open or select a document set and its document subset that contains documents for optimization.
  3. From the Process tab, in the Train group, select Optimize Content Classifier.

    The Adaptive Feature Classifier - Auto Optimization window is displayed.

  4. Optionally, click Advanced to edit the advanced options in the Optimization Settings window.
    1. Clear or select the options listed.
    2. For each option in the list, select Min. values,Max. values, and Step width.
    3. Click OK to close the Optimization Settings window.
  5. Click Start to start the optimization process.

    During optimization, each parameter is modified and tested. Optimization ends when no further improvement can be reached, or if the classification result reaches 100%.

    Note You can click Stop at any time to terminate the optimization process. Any improvements for the classification quality up to that point are saved.
  6. Click Close.

    If auto optimization was able to improve classification quality, a message is displayed prompting you to save the changes to the project.

  7. If the classifier parameters were modified, you should retrain the project to apply the new parameters to the content classifier. From the Process menu, select the relevant training option.
  8. From the Project tab, in the File group, click Save Project.
  9. Optionally, generate a classification benchmark and compare your new classification settings to previous settings.