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

The F1 score is a crucial metric in evaluating the performance of classification models, especially in situations where the distribution of classes is imbalanced. It serves as a harmonic mean of precision and recall, thus providing a single score that reflects both the model's ability to correctly identify positive instances (recall) and its capability to avoid false positives (precision).

In scenarios where one class is much more prevalent than another, a model might appear to perform well simply by predicting the majority class. Relying solely on accuracy can be deceptive in such cases. The F1 score mitigates this issue by ensuring that both precision and recall are taken into account. A high F1 score indicates that the model has a good balance, meaning it is successfully minimizing both false positives and false negatives.

This metric is particularly valuable when the cost of false negatives and false positives is unequal. For instance, in medical diagnosis, failing to identify a disease (false negative) may have more severe consequences than incorrectly diagnosing someone with it (false positive). Thus, the F1 score encourages the development of models that not only predict correctly but also maintain a balance that is critical for effective performance in real-world applications.

The other choices focus on aspects like prediction speed, temporal accuracy, or