When you want to check how well a supervised learning algorithm is working, you need to know about different metrics. One of the most important ones is called the F1 Score. This score helps balance two things: precision and recall. It's really important to know how to calculate and understand the F1 Score because it helps you see how your model is performing, especially when the classes in your data are not equal.

What are Precision and Recall?

Before we get into the F1 Score, let’s quickly go over what precision and recall mean:

• Precision: This tells us how accurate the positive predictions are. It’s the number of true positive predictions compared to the total number of predicted positives. In simpler words, it shows how many of the cases we thought were positive really were.

  $\text{Precision} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Positives (FP)}}$

• Recall: This shows us how many actual positive cases our model found. It’s the number of true positive predictions compared to all actual positive cases.

  $\text{Recall} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Negatives (FN)}}$

What is the F1 Score?

The F1 Score takes both precision and recall and puts them into one score. This is helpful when you want to have a good balance between the two. Here’s how you can calculate it:

$$\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$

This means that the F1 Score will only be high if both precision and recall are also high. If one of them is low, the F1 Score will show that.

How to Calculate the F1 Score

To find the F1 Score for your model, follow these simple steps:

1. Make Predictions: Use your model to predict outcomes for your test data.

2. Build a Confusion Matrix: Count how many True Positives, False Positives, True Negatives, and False Negatives there are. This will help with calculating precision and recall.

3. Calculate Precision: Use the precision formula to find the precision.

4. Calculate Recall: Now, use the recall formula to find the recall.

5. Compute F1 Score: Plug your precision and recall values into the F1 Score formula.

Understanding the F1 Score

The F1 Score can be anywhere from 0 to 1:

• 1 means perfect precision and recall.
• 0 means the model didn’t find any positives or incorrectly predicted all positives.

In general, a good F1 Score is above 0.5. But remember, the situation matters! In important fields like medical diagnoses, aiming for an F1 Score closer to 1 is better, since missing a positive case can have serious effects.

Conclusion

In conclusion, the F1 Score is a helpful metric that gives you more insight into how your model is doing, especially if your data isn’t evenly balanced. By learning how to calculate and interpret it alongside precision and recall, you can make better choices about which models to use in real life. Try it out in your next project, and you’ll see how great the balance it offers can be!