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

In evaluating a binary classification model, metrics such as accuracy, precision, recall, or F1 score are particularly relevant because they directly assess the performance of the model in distinguishing between the two classes (typically labeled as positive and negative).

Accuracy measures the proportion of true results (both true positives and true negatives) among the total number of cases examined. Precision focuses on the proportion of true positives among all positive predictions, providing insight into how many of the predicted positives were actually correct. Recall, or sensitivity, measures the proportion of true positives among the total actual positives, which is important for understanding the effectiveness of the model in capturing positive cases. The F1 score is the harmonic mean of precision and recall, offering a single metric that reflects both aspects, especially useful when there is an uneven class distribution.

These metrics provide a comprehensive view of the model's performance in a binary classification context, addressing different aspects of prediction quality that are crucial in real-world applications where the cost of false positives and false negatives may vary significantly.