The F1-Score is important for measuring how well a model works in supervised learning. It helps show a good balance between two ideas: precision and recall. This is especially useful when there are many more examples of one class than the other.

Why the F1-Score Matters:

1. Balance Between Metrics:

   • Precision: This tells us how accurate the positive predictions are.
     • Formula: $$\text{Precision} = \frac{TP}{TP + FP}$$
   • Recall: This checks how well the model finds all the positive samples.
     • Formula: $$\text{Recall} = \frac{TP}{TP + FN}$$
   • F1-Score: This takes both precision and recall and combines them: $$\text{F1-Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$

2. Dealing with Class Imbalance: Sometimes, there are way more negative examples than positive ones. If we only look at accuracy, it might give a false impression. For example, if we have a situation with 95% negatives and only 5% positives, a simple model that just predicts negatives would seem very accurate at 95%. But it wouldn’t find any positive cases.

3. Strong Evaluation: The F1-Score can range from 0 to 1. A score of 1 means the model has perfect precision and recall, making it a strong way to check how well the model is doing.

In short, the F1-Score is a great way to see how well a model performs on different types of data, especially when there are imbalances that we often see in real life.