F1 Score

The F1 score is a statistical measure used in machine learning and data science to evaluate the performance of binary classification models. It is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. This score ranges from 0 to 1, where 1 indicates perfect precision and recall.

Also known as: F1-Score, F1, F-measure, F1 metric, F-score

Why learn F1 Score?

Developers should learn and use the F1 score when working on imbalanced datasets or in scenarios where both false positives and false negatives are critical, such as medical diagnosis, fraud detection, or spam filtering. It is particularly useful for comparing models where accuracy alone might be misleading due to class imbalances, offering a more comprehensive view of model effectiveness.

Learning Resources

• docs: Scikit-learn Documentation: F1 Score
• tutorial: Towards Data Science: Understanding F1 Score
• course: Coursera: Machine Learning Specialization by Andrew Ng
• video: YouTube: F1 Score Explained
• book: Book: 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron