F1 Score
F1 Score is a metric used to evaluate the performance of a binary classification model. It is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives. F1 Score provides a balance between precision and recall, and is a better measure than accuracy when dealing with imbalanced datasets. A high F1 Score indicates that the model has both high precision and high recall, while a low F1 Score indicates that the model is either biased towards precision or recall.
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