Understanding Recall in Machine Learning

Recall is really important when we look at how well machine learning models work. This is especially true in cases where the data is imbalanced. In simple terms, when some categories are much larger than others.

In these cases, missing a positive match—called a false negative—can cause serious problems.

But recall depends on two things:

1. True positives (the correct matches we found)
2. The total number of actual positives (how many there should be)

This means that if our data quality isn't good or if the data is not spread out evenly, we might get misleading results.

Here are some challenges we face with recall:

• It can be hard to get a high recall while also maintaining accuracy, called precision.
• If the different categories in the data are not balanced, it can confuse our recall measurements.

To tackle these issues, we can use a few helpful techniques:

• Resampling: Changing our data to make the groups more balanced.
• Threshold adjustment: Changing the cutoff point used to decide what counts as a match.
• Composite metrics: Using combined measurements, like the F1 Score, which helps give a better overall view.

By using these methods, we can get a clearer picture of how well our models are really performing.