What is the F1 Score?

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Multiple Choice

What is the F1 Score?

F1 score is the harmonic mean of precision and recall, combining the two to balance false positives and false negatives in a single measure. Precision is the proportion of predicted positives that are truly positive (TP / (TP + FP)), and recall is the proportion of actual positives that were captured (TP / (TP + FN)). The F1 score is calculated as F1 = 2 × (precision × recall) / (precision + recall). It rewards a model only when both precision and recall are high, and it drops if either one is low. This makes it especially useful when you’re dealing with imbalanced data or when you care about both types of errors. It’s not the same as accuracy, which can be misleading when one class dominates, and it’s not a ROC curve, which is a plot of true positive rate versus false positive rate across thresholds rather than a single metric. For example, if precision is 0.8 and recall is 0.6, the F1 score is about 0.69, reflecting the trade-off between the two.

F1 score is the harmonic mean of precision and recall, combining the two to balance false positives and false negatives in a single measure. Precision is the proportion of predicted positives that are truly positive (TP / (TP + FP)), and recall is the proportion of actual positives that were captured (TP / (TP + FN)). The F1 score is calculated as F1 = 2 × (precision × recall) / (precision + recall). It rewards a model only when both precision and recall are high, and it drops if either one is low. This makes it especially useful when you’re dealing with imbalanced data or when you care about both types of errors. It’s not the same as accuracy, which can be misleading when one class dominates, and it’s not a ROC curve, which is a plot of true positive rate versus false positive rate across thresholds rather than a single metric. For example, if precision is 0.8 and recall is 0.6, the F1 score is about 0.69, reflecting the trade-off between the two.

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