Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What type of model can utilize multiple labels on the same data?

1. Regression model

2. Binary classification model

3. Multilabel classification model

4. Unsupervised clustering model

The correct answer is: Multilabel classification model

A multilabel classification model is specifically designed to handle scenarios where each instance of data can be associated with multiple labels simultaneously. This capability allows the model to recognize and predict more than one category for a single data point, making it particularly useful in applications such as image tagging, where an image might be labeled as containing both "dog" and "park," or in text classification, where a document might belong to multiple topics. By contrast, a regression model predicts a continuous value rather than classifying data into discrete categories. A binary classification model is limited to predicting one of two possible labels for each instance, and therefore cannot accommodate multiple labels for the same data point. Unsupervised clustering models group data based on similarities but do not assign labels to individual instances in the same way multilabel classification does.