Recall

In machine learning, evaluating the performance of a model is essential to ensure its effectiveness and reliability. Among various metrics used for classification problems, the F1 Score is one of the most important and widely used. This metric helps assess the balance between precision and recall, providing a score reflecting the model's accuracy and ability to identify relevant instances. What is the F1 Score? The F1 Score measures a model's accuracy, the harmonic mean of precision and recall. It considers both false positives and false negatives, making it especially useful when class distribution is imbalanced. The formula for F1 Score [...]