Tutorial / Cram Notes
Classifiers are tools that categorize data into certain types. Microsoft provides several out-of-the-box classifiers for common types of sensitive information, such as credit card numbers, social security numbers, and more. However, organizations often have unique data that aren’t adequately recognized by these default classifiers. To address this, Microsoft allows administrators to create their own custom classifiers.
Creating a Custom Classifier
To create a custom classifier, you must provide examples of the type of content you want to classify. This process is known as “training.” Initially, you will label a set of documents as examples of the category you want to identify. The classifier then uses this training set to learn and make predictions about unlabeled data.
Training Process
- Define the Classifier: Decide on the categories you want to classify, for instance, “Internal Projects” or “Client Contracts”.
- Select Data: Gather a representative sample set of documents (minimum of 50 is recommended) for each category.
- Label Data: Manually classify the selected data sample to train the classifier.
- Train the Classifier: Use the labeled dataset to train the classifier within the Microsoft 365 compliance center.
- Test the Classifier: Evaluate the effectiveness of the classifier on a separate set of documents.
- Publish the Classifier: Once tested, the classifier can be published and used across the organization.
Retraining a Classifier
Even after a machine learning model is deployed, it may require periodic updates to maintain or improve its accuracy. This process is known as “retraining”.
When to Retrain a Classifier
- Drift in Data: When the nature of data changes over time, leading to a misclassification of data.
- Feedback from Users: User flags indicating the classifier made an incorrect prediction can prompt retraining.
- Regulatory Changes: New compliance requirements could necessitate updating classifiers.
Steps for Retraining
- Review Misclassified Documents: Collect examples where the classifier has made errors.
- Curate Additional Training Set: Obtain new examples of correctly classified documents and misclassified ones.
- Label and Add Data: Update the training set by labeling the new examples.
- Train the Model: Use the enriched training set to train the model again.
- Evaluate Performance: Measure the performance of the updated classifier against a test set.
- Iterate as Necessary: Repeat the retraining process until satisfactory accuracy is reached.
- Deploy the Updated Classifier: Release the retrained classifier for organizational use.
Monitoring Classifiers and Retraining
Regularly monitoring the performance of classifiers is a key administrative task. Microsoft provides tools within the compliance center to track effectiveness, enabling admins to decide when to retrain classifiers.
Real-world Example
Imagine an organization dealing with proprietary chemical formulas. The default classifiers might not identify these as sensitive data. A custom classifier is created with labeled examples of these formulas. Over time, the structure or format of these formulas changes, and users report misclassifications. The classifier is retrained with new examples reflecting the recent changes to ensure that this sensitive information remains correctly identified and protected.
Retraining classifiers is an essential task in the ongoing management of an organization’s data protection strategy. With the SC-400 Microsoft Information Protection Administrator exam, Microsoft aims to equip administrators with the knowledge and skills required to effectively implement, manage, and maintain these important information protection techniques.
Practice Test with Explanation
Retraining a classifier requires a new dataset that is representative of all classes the classifier is expected to detect.
- True
- False
Answer: True
Explanation: When retraining a classifier, it is essential to use a dataset that is representative of all classes to ensure that the classifier performs well across all expected categories.
The only method to improve a classifier’s accuracy is retraining it with a larger dataset.
- True
- False
Answer: False
Explanation: While using a larger dataset can help, other methods like feature engineering, model tuning, and incorporating different algorithms can also improve a classifier’s performance.
Microsoft Information Protection (MIP) requires manual retraining of classifiers for optimal performance.
- True
- False
Answer: True
Explanation: Manual retraining of classifiers within MIP may be necessary when changes in data patterns are detected or when the initial classifier performance does not meet expectations.
Which of the following are reasons you might retrain a classifier? (Select all that apply)
- [A] Previous model is not generalizing well to new data
- [B] Regulatory compliance updates
- [C] Introduction of new classes to be classified
- [D] No reasons, classifiers do not need retraining
Answer: [A], [B], [C]
Explanation: Classifiers may need retraining to cope with new data, comply with regulatory updates, or classify new types of information.
Continuous (automatic) retraining of classifiers is a feature available in all versions of Microsoft Information Protection.
- True
- False
Answer: False
Explanation: Continuous retraining of classifiers is not necessarily a standard feature across all versions of Microsoft Information Protection and may require configuration or additional services.
When retraining a classifier, all previous training data must be discarded.
- True
- False
Answer: False
Explanation: Previous training data can often be used in conjunction with new data to provide a more robust training set for the classifier.
Which of the following is a crucial step before retraining a classifier?
- [A] Evaluating the classifier’s current performance
- [B] Deleting existing security policies
- [C] Training a new classifier from scratch
- [D] Increasing the IT budget
Answer: [A]
Explanation: Evaluating the current performance of the classifier is essential to understand the need and scope for retraining.
The primary goal of retraining a classifier in Microsoft Information Protection is to:
- [A] Increase the speed of classification
- [B] Improve accuracy and reduce false positives/negatives
- [C] Cut costs on data storage
- [D] Make it more user-friendly
Answer: [B]
Explanation: The primary goal of retraining a classifier is to improve its accuracy and reduce false positives and negatives, which enhances the overall effectiveness of information protection.
Retraining a classifier on the same dataset repeatedly will always result in a better model.
- True
- False
Answer: False
Explanation: Retraining on the same dataset can lead to overfitting, where the model performs well on known data but poorly on unseen data.
In Microsoft Information Protection, sensitive information types (SITs) are classified using:
- [A] Machine learning classifiers only
- [B] Rules and regular expressions only
- [C] A combination of machine learning classifiers and rules/regular expressions
- [D] User behavior analytics only
Answer: [C]
Explanation: Microsoft Information Protection uses a combination of machine learning classifiers, rules, and regular expressions to identify and classify sensitive information types (SITs).
After retraining a classifier, what is a recommended practice?
- [A] Immediate deployment to production
- [B] Conducting a thorough validation/test phase
- [C] Sharing the classifier model publicly
- [D] Eliminating all user feedback mechanisms
Answer: [B]
Explanation: It is recommended to conduct a thorough validation or test phase after retraining to ensure the updated classifier performs as expected before deploying to production.
Who is responsible for retraining classifiers in Microsoft Information Protection?
- [A] Only the IT department
- [B] Data scientists exclusively
- [C] Any user with appropriate permissions
- [D] Compliance and security officers
Answer: [C]
Explanation: Retraining classifiers can be the responsibility of any user with the appropriate permissions within Microsoft Information Protection, not exclusively any one department or role.
Interview Questions
What is Content Explorer in Microsoft 365?
Content Explorer is a feature in Microsoft 365 that enables organizations to view and manage sensitive data in their organization.
What is a classifier in Microsoft 365’s Content Explorer?
A classifier in Microsoft 365’s Content Explorer is a machine learning tool that identifies and classifies sensitive information within digital documents.
Why is it necessary to retrain a classifier in Microsoft 365’s Content Explorer?
It is necessary to retrain a classifier in Microsoft 365’s Content Explorer to ensure that it remains accurate and effective over time.
What are some reasons that can cause a classifier’s accuracy to degrade over time?
Changes in data or updates to classification rules can cause a classifier’s accuracy to degrade over time.
What is the first step to retrain a classifier in Microsoft 365’s Content Explorer?
The first step to retrain a classifier in Microsoft 365’s Content Explorer is to open Content Explorer.
What is the second step to retrain a classifier in Microsoft 365’s Content Explorer?
The second step to retrain a classifier in Microsoft 365’s Content Explorer is to select the classifier that needs to be retrained.
What is the third step to retrain a classifier in Microsoft 365’s Content Explorer?
The third step to retrain a classifier in Microsoft 365’s Content Explorer is to choose Retrain from the classifier’s context menu.
What is the fourth step to retrain a classifier in Microsoft 365’s Content Explorer?
The fourth step to retrain a classifier in Microsoft 365’s Content Explorer is to select the data source that will be used to retrain the classifier.
What is the fifth step to retrain a classifier in Microsoft 365’s Content Explorer?
The fifth step to retrain a classifier in Microsoft 365’s Content Explorer is to specify the training parameters, such as the percentage of documents to use for training and the number of iterations.
What is the sixth step to retrain a classifier in Microsoft 365’s Content Explorer?
The sixth step to retrain a classifier in Microsoft 365’s Content Explorer is to start the retraining process.
How can an organization monitor the results of the retraining process of a classifier in Microsoft 365’s Content Explorer?
An organization can monitor the results of the retraining process of a classifier in Microsoft 365’s Content Explorer to identify errors and areas where the classifier needs improvement.
What should an organization do if a classifier in Microsoft 365’s Content Explorer is not accurately identifying and classifying sensitive information?
If a classifier in Microsoft 365’s Content Explorer is not accurately identifying and classifying sensitive information, the organization should refine and adjust the classifier as needed.
What are some best practices for retraining a classifier in Microsoft 365’s Content Explorer?
Best practices for retraining a classifier in Microsoft 365’s Content Explorer include using a representative sample of training data, monitoring the results, refining and adjusting the classifier, and retesting it.
How can an organization ensure that their classifier in Microsoft 365’s Content Explorer remains accurate and effective over time?
An organization can ensure that their classifier in Microsoft 365’s Content Explorer remains accurate and effective over time by retraining it as needed and following best practices for retraining.
Can a classifier in Microsoft 365’s Content Explorer be retrained for multiple languages?
Yes, a classifier in Microsoft 365’s Content Explorer can be retrained for multiple languages.
Just finished reading the post, and it helped clarify a lot about retraining classifiers for the SC-400 exam. Thanks!
I’m wondering about the specific challenges faced when retraining a classifier. Any insights?
Does anyone have any tips for optimizing the training data?
Can we use pre-trained models for the SC-400 exam, or do we have to start from scratch?
Great post! It really simplifies the retraining process for beginners.
How often should we retrain classifiers to keep up with evolving data patterns?
I found the post too basic. More advanced stuff would be appreciated.
Do we need to retrain the entire model, or are there incremental methods available?