IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

A confusion matrix primarily serves to display the relationship between actual and predicted classifications. It is a table used in supervised learning to evaluate the performance of a classification model. The matrix compares the actual labels of the data against the predicted labels produced by the model, thereby providing insights into not just the accuracy of the model, but also how well it is performing across different classes.

The four quadrants of a confusion matrix typically indicate true positives, true negatives, false positives, and false negatives. This allows data scientists to easily see where the model is making errors, prompting deeper analysis into specific classes that may be misclassified.

While calculating performance metrics is indeed a function related to confusion matrices, such as deriving accuracy, precision, recall, and F1-score, these calculations are secondary outcomes derived from the relationship illustrated by the confusion matrix itself. Therefore, the fundamental role of the confusion matrix is to provide that direct comparison of predicted vs. actual classifications, making the chosen answer the most precise in capturing its core purpose.