Procedure: Schedule training using the Model Options tab
This topic describes part of the functionality of Genesys Content Analyzer.
Purpose: To specify how and when a training object will be processed to produce a model.
- A training object containing a sufficient number of e-mails or other text objects. When to Train provides suggestions about judging whether there are enough text objects.
- Model Name —Enter a name for the model that will result from the scheduled training. Creating a Category Tree explains restrictions on the names of Knowledge Manager objects.
- Training Object —Select a training object.
- Subject Field Treatment —Select from the following treatments of the Subject field of e-mails:
- Ignore —Training does not consider the content of the Subject field
- Add to the text —Training considers the content of the Subject field.
- Add with double weight —Training gives the content of the Subject field twice as much importance as the content of the e-mail body.
- Training time increases as you move from Draft quality to level 3 quality. But once the quality goes above 3, there is not much difference in training time.
- Genesys recommends that you use Draft quality only when you want to obtain a preliminary reading of the model’s quality estimation. For production, use quality 2–6.
If you select cross-validation, training produces an accuracy rating for the model along with the model itself. This has the advantage of not requiring an extra testing step, but it increases the training time.
ImportantBe sure to set a time later than the present moment.
A relatively high value for this setting can reduce training time, but it can also reduce quality. What counts as a high or low value for this setting depends on the total size of the training object. For example, if a training object has 5 to 10 text objects per category, a high keyword threshold might be 2 or3. If a training object has 30 to 50 text objects per category, a high keyword threshold might be 20.
- Optionally, remove superfluous or misleading text from the training object (next section).
- Once the model is trained, test it. See Testing Models.
This page was last modified on December 17, 2013, at 11:54.