The F1 score is a way to measure how well a classification model is performing, especially when we care about both the accuracy of its positive predictions and how well it captures all the positive cases. It’s a balance between two key concepts:
- Precision: This tells you how many of the items the model identified as positive are actually correct. For example, in a spam filter, precision would measure how many of the emails marked as spam are really spam.
- Recall: This measures how well the model captures all the actual positive cases. In the spam filter example, recall would tell you how many of the real spam emails the model correctly identified.
The F1 score combines these two measurements into one. It’s useful when you want to strike a balance between precision and recall, especially in situations where the data is imbalanced (for example, where there are many more negative cases than positive ones). A higher F1 score means the model is good at both catching positives and avoiding false positives, making it an important metric in fields like medical diagnoses, fraud detection, or spam filtering.
In short, the F1 score helps evaluate a model’s overall effectiveness by taking both precision and recall into account, rather than focusing on just one.
