TLDR: The F1 score is a key metric for evaluating machine learning classification models, balancing precision and recall to avoid false positives/negatives. Important for imbalanced datasets in applications like fraud detection and medical diagnosis. Calculated as the harmonic mean of precision and recall, it provides insightful performance measures, though it has limitations such as neglecting true negatives and being less suitable for some datasets.
F1 Score in Machine Learning: How to Calculate, Apply, and Use It Effectively
