Tutorial / Cram Notes
Trainable classifiers in Microsoft 365 use machine learning to recognize various types of content. They can be pre-built by Microsoft or customized for your organizational needs. Before using a trainable classifier to apply labels or policies, it is imperative to test its accuracy to ensure that it correctly identifies the relevant content.
Steps to Test a Trainable Classifier
To test a trainable classifier, you would typically follow these steps:
- Define your classifier and choose the type of content you want to identify.
- Seed the classifier with example items that are representative of the content.
- Train the classifier using the provided examples.
- Test the classifier’s efficacy using a new, separate set of content not used in training.
- Review the results and make adjustments as needed.
When testing a classifier, remember that the initial results may not be perfect. It may take several iterations of training and testing to improve accuracy.
Example of Testing a Classifier
Suppose you want to classify confidential project documents. After defining and seeding your classifier with examples of project documents, you would then extract a set of sample files that the classifier has not seen before for testing. These might include:
- Actual project documents (positive examples)
- Non-project-related documents (negative examples)
You would then run the classifier on this test set and review the results, looking for:
- True Positives: Correctly classified project documents.
- True Negatives: Correctly identified non-project documents.
- False Positives: Non-project documents misclassified as project documents.
- False Negatives: Project documents not recognized by the classifier.
Reviewing Test Results
The results from the test run can be tabulated as follows for better clarity:
| Project Document (Actual) | Non-Project Document (Actual) | |
|---|---|---|
| Classified as Project Document | True Positive (TP) | False Positive (FP) |
| Classified as Non-Project Document | False Negative (FN) | True Negative (TN) |
Using these results, you can calculate metrics such as precision, recall, and F1 score to evaluate the performance of the classifier:
- Precision (Positive Predictive Value): \( \frac{TP}{(TP + FP)} \)
- Recall (True Positive Rate): \( \frac{TP}{(TP + FN)} \)
- F1 Score: \( 2 * \frac{\text{Precision * Recall}}{\text{(Precision + Recall)}} \)
Adjusting and Retraining the Classifier
Based on the test results, adjustments may be necessary. These could include adding more examples to the training set, redefining the parameters of what constitutes a “project document,” or tweaking the machine learning model settings.
Analyze which types of documents are misclassified and look for patterns. For example, if your false positives include a lot of financial documents, you might need to adjust the classifier to differentiate between financial documents and confidential project papers better.
Once you’ve made changes, retrain the classifier with the updated dataset and test it again using a new test set.
Implementing the Classifier
After satisfactory test results, the classifier can be deployed within the organization. However, it’s important to continue monitoring its performance, as types of content and organizational needs may evolve over time, necessitating further adjustments to the classifier.
Remember that testing a trainable classifier is an iterative process, and continuous improvement is key to maintaining a high level of accuracy in content classification. This functionality plays an important role in data governance and protection within the Microsoft 365 ecosystem as outlined in the SC-400 exam objectives.
Practice Test with Explanation
True or False: When testing a trainable classifier, you only need a small set of items as a sample.
- False
When testing a trainable classifier, it is recommended to use a larger and diverse set of items to ensure the classifier can be accurately evaluated across different content types.
When should you train a classifier in Microsoft 365 compliance center?
- a) Before creating any labels
- b) After creating labels and before applying them to content
- c) After applying labels to content
- d) It is not necessary to train a classifier
b) After creating labels and before applying them to content
You should train a classifier after creating labels but before applying them to content to ensure that the classifier can effectively identify the content that matches the label’s criteria.
True or False: For a trainable classifier to properly learn, it should be exposed only to items that are very similar in content.
- False
A trainable classifier should be exposed to a variety of items, including both positive and negative examples, to learn effectively and generalize well across different types of content.
How many phases are involved in the lifecycle of a trainable classifier in Microsoft Information Protection?
- a) One
- b) Two
- c) Three
- d) Four
c) Three
The lifecycle of a trainable classifier involves three phases: training, testing, and tuning.
True or False: After testing a trainable classifier, it can be immediately used in policies without any further adjustments.
- False
After testing a trainable classifier, it may require further tuning based on test results to improve accuracy before it is used in policies.
Which of the following is a metric used to evaluate the performance of a trainable classifier?
- a) Number of documents scanned
- b) Classification speed
- c) Precision and recall
- d) Total data size
c) Precision and recall
Precision and recall are metrics used to evaluate the performance of a trainable classifier, where precision measures the accuracy of positive predictions and recall measures the classifier’s ability to find all relevant items.
True or False: A trainable classifier can differentiate between sensitive and non-sensitive content on its own, without any seed data.
- False
A trainable classifier requires seed data, which consists of positive and negative examples, to learn how to differentiate between sensitive and non-sensitive content.
What is a recommended practice when selecting a sample set for testing a trainable classifier?
- a) Choose only the recently created items
- b) Use a sample set that is not relevant to the classifier’s intended purpose
- c) Include a random set of items
- d) Select a representative set of items that includes examples of what the classifier should and should not match
d) Select a representative set of items that includes examples of what the classifier should and should not match
A representative set of items is necessary for effective testing so the classifier can be evaluated on how well it performs in identifying correct matches and ignoring non-matches.
True or False: It is possible to use custom trainable classifiers with Microsoft’s built-in classification types.
- True
Microsoft allows the use of custom trainable classifiers in conjunction with the built-in classification types provided within the compliance center. This can help organizations tailor classification efforts to their specific needs.
How many positive and negative examples should you provide to the trainable classifier during the testing phase?
- a) At least 50 positive and 50 negative examples
- b) At least 10 positive and 10 negative examples
- c) At least 500 positive and 500 negative examples
- d) The number of examples does not matter as long as they are high-quality
a) At least 50 positive and 50 negative examples
It is recommended to provide at least 50 positive and 50 negative examples during the testing phase to give the classifier a sufficient diversity of content to evaluate its performance.
Interview Questions
What is a trainable classifier in Microsoft 365’s Information Protection feature?
A trainable classifier is a machine learning tool that can be used to identify and classify sensitive information within digital documents.
Why is it important to test a trainable classifier in Microsoft 365?
It is important to test a trainable classifier in Microsoft 365 to ensure that it can accurately identify and classify sensitive information within digital documents.
What is the first step in testing a trainable classifier in Microsoft 365?
The first step in testing a trainable classifier in Microsoft 365 is to collect a set of test data that includes a variety of documents that contain the sensitive information the classifier is designed to identify.
What should the test data for a trainable classifier in Microsoft 365 include?
The test data for a trainable classifier in Microsoft 365 should include both positive and negative examples of the sensitive information, so the classifier can be tested in a variety of situations.
What is the second step in testing a trainable classifier in Microsoft 365?
The second step in testing a trainable classifier in Microsoft 365 is to run the classifier against the test data to identify and classify the sensitive information.
What is the third step in testing a trainable classifier in Microsoft 365?
The third step in testing a trainable classifier in Microsoft 365 is to analyze the results of the test to determine the accuracy of the classifier.
What should an organization do if a trainable classifier in Microsoft 365 has a high false positive or false negative rate?
If a trainable classifier in Microsoft 365 has a high false positive or false negative rate, the organization should refine or adjust the classifier.
What is the fourth step in testing a trainable classifier in Microsoft 365?
The fourth step in testing a trainable classifier in Microsoft 365 is to refine and adjust the classifier based on the results of the test.
What is the final step in testing a trainable classifier in Microsoft 365?
The final step in testing a trainable classifier in Microsoft 365 is to retest the classifier to ensure that the changes have improved its accuracy and effectiveness.
What are some best practices for testing a trainable classifier in Microsoft 365?
Best practices for testing a trainable classifier in Microsoft 365 include using a representative sample of test data, monitoring the results, refining and adjusting the classifier, and retesting it.
Can a trainable classifier in Microsoft 365 be tested for multiple languages?
Yes, a trainable classifier in Microsoft 365 can be tested for multiple languages.
How can an organization ensure that their trainable classifier in Microsoft 365 is accurate and effective?
An organization can ensure that their trainable classifier in Microsoft 365 is accurate and effective by following best practices for testing and refining it.
Can a trainable classifier in Microsoft 365 be used in conjunction with other security measures?
Yes, a trainable classifier in Microsoft 365 can be used in conjunction with other security measures to protect sensitive information.
How does Microsoft 365 ensure the security of sensitive information when using trainable classifiers?
Microsoft 365 ensures the security of sensitive information when using trainable classifiers through the use of encryption, access control, and auditing.
Can a trainable classifier in Microsoft 365 be shared with other organizations?
Yes, a trainable classifier in Microsoft 365 can be shared with other organizations.
Just tested a trainable classifier for the SC-400 exam. It worked better than I expected!
For me, the labeling process was the toughest part. Any suggestions on streamlining it?
Appreciate the blog post!
I found that the accuracy improved drastically with more iterations. Has anyone else experienced this?
I’m struggling with false positives. Any advice?
The documentation on trainable classifiers could be improved.
I just aced the SC-400 exam! The trainable classifier section was a breeze thanks to this blog.
Which algorithms are best suited for trainable classifiers in this context?