F1-Score

The F1-score is a way to measure how accurate a model is, considering both precision and recall. Precision tells you how many of the positive predictions made by the model were actually correct. Recall tells you how many of the actual positive cases the model was able to identify. The F1-score balances these two metrics, giving a single score that represents the overall performance of the model. It's calculated as the harmonic mean of precision and recall:F1-Score = 2 * (Precision * Recall) / (Precision + Recall)

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• @article@F1-score | scikit-learn