The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.

The F1 score is a metric used to evaluate the performance of a classification model. It is the harmonic mean of precision and recall. Precision measures the proportion of true positive predictions out of all positive predictions made by the model, while recall measures the proportion of true positives that were correctly identified by the model out of all actual positives in the data.

The formula for F1 score is:

$F 1 = 2 \times P rec i s i o n + R ec a ll P rec i s i o n \times R ec a ll$

The F1 score ranges from 0 to 1, where a higher score indicates better performance. It is useful when the class distribution is imbalanced, meaning one class may dominate the data over others. It gives equal weight to both precision and recall, making it a suitable metric when you want to balance between these two metrics.

In a machine learning interview, you could answer the question like this:

“The F1 score is a metric commonly used in classification tasks to assess the model’s performance. It is the harmonic mean of precision and recall, providing a balance between these two metrics. This is particularly useful when we have imbalanced class distributions, where one class may be significantly more prevalent than others. By considering both precision and recall, the F1 score gives a holistic view of the model’s ability to correctly identify positive instances while minimizing false positives and false negatives. In practice, we can use the F1 score to compare different models and choose the one that strikes the best balance between precision and recall for our specific problem domain.”