Definition
The F1 score is a metric used in machine learning to evaluate the performance of a classification model. It represents the harmonic mean of precision and recall, providing a single score that balances both false positives and false negatives. In practice, the F1 score is calculated using the results of a model's predictions on a dataset, comparing them to the actual known outcomes. This metric is crucial for understanding a model's effectiveness, especially when dealing with imbalanced datasets where accuracy alone can be misleading. For instance, in a medical diagnosis system, a high F1 score indicates the model is good at both identifying actual cases of a disease (recall) and avoiding misdiagnosing healthy individuals (precision). It differs from accuracy by considering the balance between false positives and false negatives, whereas accuracy simply looks at the overall correct predictions. You will encounter the F1 score in AI and machine learning certification exams, including those focused on data science and predictive modeling.