Final answer:
A good F-1 score in binary classification represents a strong balance between precision and recall, implying that the model performs well in correctly predicting positive and negative instances. Precision measures correctness of positive predictions and recall measures coverage of actual positive instances.
Step-by-step explanation:
In binary classification, a good F-1 score indicates option D, a balance between precision and recall, with higher values indicating better performance. Precision refers to the number of true positive predictions divided by the number of true positive plus false positive predictions, showing how many of the classified positive instances are actually positive.
Recall, or sensitivity, measures the number of true positive predictions divided by the number of true positive plus false negative predictions, indicating how many of the actual positive instances were correctly predicted. A high F-1 score shows that the classifier has a robust performance in terms of both precision and recall, indicating fewer misclassifications for both positive and negative instances.
Consider the concepts of accuracy and precision in a broader sense. Accuracy refers to how close a measurement is to the true value, while precision refers to the consistency of repeated measurements. In the context of binary classification, accuracy reflects the proportion of correct predictions (both true positives and true negatives) among all predictions, whereas precision, as mentioned earlier, is specifically about correct positive predictions.