AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The F1-score is a crucial metric for evaluating the performance of a classification model, particularly when dealing with imbalanced datasets. It provides a way to balance precision and recall, which are two fundamental aspects of model performance.

Precision measures the proportion of true positive predictions among all positive predictions, while recall evaluates the proportion of true positives among all actual positive cases. In situations where false positives and false negatives have different implications (for example, in medical diagnosis or fraud detection), relying on precision or recall alone may not provide a full picture of the model's effectiveness.

The F1-score is calculated as the harmonic mean of precision and recall. This means it considers both metrics equally and highlights their balance. A high F1-score indicates that both precision and recall are reasonably high, indicating that the model is performing well in terms of both avoiding false positives and identifying true positives.

Other options, such as AUC-ROC, focus on the trade-off between true positive rates and false positive rates but do not directly address precision and recall in the same manner. Mean Squared Error is primarily used for regression models, and Cross-entropy loss is often associated with the training process for classification tasks but does not serve as a direct measure of a model’s precision