In the real world, when we use supervised learning algorithms, we often face a tricky choice between two important ideas: precision and recall.

Precision tells us how accurate our positive predictions are. It’s like checking how many of the things we thought were true really are true. We can calculate precision like this:

$\text{Precision} = \frac{TP}{TP + FP}$

Here, TP stands for true positives (things we got right) and FP is false positives (things we mistakenly thought were right).

Recall, on the other hand, helps us understand how well our model finds all the things that matter. We can figure out recall with this formula:

$\text{Recall} = \frac{TP}{TP + FN}$

In this case, FN means false negatives (things we missed that we should have found).

Let’s think about a couple of examples.

In fraud detection, we want high precision. This means we want to make sure that when we say something is fraud, we are usually right. If we have high precision, we might miss some actual fraud cases (which lowers recall).

Now, in medical tests, we would want high recall. This means we want to catch every possible sickness. But with high recall, we could also end up with many false alarms, where we say someone is sick when they’re not (which lowers precision).

Finding the right balance between precision and recall depends on the situation.

To help with this, we can use an F1 Score, which combines both precision and recall:

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

By balancing precision and recall, we can use our models effectively in different real-life situations.