In which scenario is the F1 Score particularly useful?

In which scenario is the F1 Score particularly useful?

Step 1: Understand what F1 Score is. It is a measure that combines precision and recall into one number.
Step 2: Know what precision means. Precision is the number of true positive results divided by the number of all positive results predicted by the model.
Step 3: Know what recall means. Recall is the number of true positive results divided by the number of actual positive results in the data.
Step 4: Recognize that in some situations, one class (like 'yes' or 'no') may have many more examples than the other class. This is called an imbalanced class distribution.
Step 5: Understand that in imbalanced situations, accuracy alone can be misleading. For example, if 95% of your data is one class, a model that always predicts that class can still seem accurate but is not useful.
Step 6: Realize that F1 Score helps by providing a better measure of a model's performance when the classes are imbalanced, as it takes both precision and recall into account.

No concepts available.