AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The ideal classifier based on F1-score is one with an F1-score close to one. The F1-score is a metric that balances precision and recall and is particularly useful in situations where there is an imbalanced class distribution. A score of one indicates perfect precision and recall, meaning the classifier is making no false positive or false negative predictions.

When evaluating classifiers, the goal is to maximize the F1-score. A higher F1-score suggests that the classifier has a better performance regarding the trade-off between true positives and false positives, as well as true positives and false negatives. This is crucial in many applications, such as medical diagnosis or fraud detection, where both false negatives and false positives can have significant consequences.

The other scores represent less desirable performance levels. A score close to zero would indicate poor performance, while a score of around 0.5 suggests that the classifier is only modestly effective, typically showing a significant number of incorrect predictions. Although a score of 0.75 demonstrates reasonable performance, it is not as optimal as achieving a score close to one. Therefore, aiming for an F1-score close to one is essential to ensure the classifier is as effective as possible.