Microsoft Azure AI Fundamentals (AI-900) Practice Exam

In evaluating a model, what is the purpose of examining the values of a confusion matrix?

1. To measure feature importance

2. To analyze model performance

3. To conduct feature selection

4. To evaluate data preprocessing steps

The correct answer is: To analyze model performance

Examining the values of a confusion matrix serves a critical function in analyzing model performance, particularly in classification tasks. The confusion matrix provides a detailed breakdown of the model's predictions compared to the actual outcomes. It categorizes the results into four key areas: true positives, false positives, true negatives, and false negatives. By evaluating these values, one can derive important metrics such as accuracy, precision, recall, and F1 score, which are essential for understanding how well the model is performing. These metrics help identify whether the model is correctly identifying each class and where it may be making errors. For instance, high numbers of false positives or false negatives can indicate specific weaknesses in the model that need addressing. This detailed performance analysis allows data scientists and machine learning practitioners to assess the effectiveness of the model, identify areas for improvement, and make informed decisions about tuning the model or selecting different algorithms.