Trainable classifier not working after being published

jpcapone 1,491

I wrote a script that creates random employee records including PII and exports it out to Excel. I uploaded over 100 Excel files for seeding data to create a trainable classifier. After the classifier was published, it gives this error: "employee_records_20241028_142530_1.xlsx" does not contain "Test_PII_A" when the classifier is tested. I added a a screenshot of what was used for seeding and testing. The file used for testing is exactly the same as the seeded information. Any ideas as to why this would happen?

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jpcapone 1,491 Reputation points

2024-10-28T19:19:41.67+00:00

I just tested one of the files individually using an SIT and that doesn't work either. So it has to have something to do with the way the files are generated?
phemanth 11,125 Reputation points Microsoft Vendor

2024-10-29T08:45:40.2133333+00:00
@jpcapone

Thanks for reaching out to Microsoft Q&A.

It looks like you're encountering issue with your trainable classifier in Microsoft Purview after publishing it.

Here are some potential reasons for the error and steps to troubleshoot the problem:

Data Format and Structure: Ensure that the structure of the seeded Excel files matches the expected format for the classifier. Check for any discrepancies in column names, data types, or formatting that might prevent the classifier from recognizing the PII.

Content Variation: If the seeded data is randomly generated, ensure that the specific term "Test_PII_A" is present in the files exactly as it was defined in the classifier's training data. Even slight variations (like extra spaces, different casing, etc.) can cause the classifier to fail in recognizing the term.

Classifier Configuration: Double-check the configuration of your classifier. Ensure that the keywords or patterns you're trying to detect are correctly defined and that the classifier is set up to look for them in the right context.

Testing Methodology: When testing the classifier, make sure you are using the same context and method as when you seeded the data. If you used specific options or settings during the training phase, replicate those during testing.

SIT (Sample Input Test): Since you mentioned testing with an SIT, ensure that the SIT is correctly configured and that it contains the same data as your seeded records. If it doesn't work with the SIT, this might indicate an issue with the classifier's ability to recognize the data.

Re-Training the Classifier: If all else fails, consider re-training the classifier with a smaller, controlled dataset that you know works. Gradually increase the complexity of the dataset to identify any issues.

Hope this helps. Do let us know if you any further queries.
jpcapone 1,491 Reputation points

2024-10-29T13:54:54.8566667+00:00
Content Variation: If the seeded data is randomly generated, ensure that the specific term "Test_PII_A" is present in the files exactly as it was defined in the classifier's training data. Even slight variations (like extra spaces, different casing, etc.) can cause the classifier to fail in recognizing the term.

Are you saying that the name of trainable classifier needs to be present within the seeded data? The screenshot was representative of the seeded data. Shouldn't a document with the same format be identified by the trainable classifier after it is published?
phemanth 11,125 Reputation points Microsoft Vendor

2024-10-30T08:32:18.24+00:00
@jpcapone I understand your concern! To clarify, the name of the trainable classifier itself doesn’t need to be present in the seeded data. Instead, what matters is that the specific terms or patterns you want the classifier to recognize (like “Test_PII_A”) are included in the seeded data exactly as defined during training.

If your document format is consistent and matches what was used for training, it should ideally be identified by the classifier after publication. However, if the classifier is still failing to recognize the data, it could be due to:

Subtle Differences: Even small variations in the data (like extra spaces or different casing) can affect recognition.

Classifier Configuration: Ensure that the classifier is set up to look for the correct patterns in the right context.

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Trainable classifier not working after being published

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