Let’s have a look at this table before directly jumping into the F1 score.
| Prediction |
Predicted Yes |
Predicted No |
| Actual Yes |
True Positive (TP) |
False Negative (FN) |
| Actual No |
False Positive (FP) |
True Negative (TN) |
In binary classification we consider the F1 score to be a measure of the model’s accuracy. The F1 score is a weighted average of precision and recall scores.
F1 = 2TP/2TP + FP + FN
We see scores for F1 between 0 and 1, where 0 is the worst score and 1 is the best score.
The F1 score is typically used in information retrieval to see how well a model retrieves relevant results and our model is performing.
