Why should you care about precision and recall?
In the field of AI, precision and recall are essential measures of prediction performance across various scenarios.
In a perfect world, you want your prediction system to be getting both high precision and high recall. However, that almost never happens. One or the other will have to be prioritized, but both should be optimized.
The discussion around these metrics hinges on one fundamental question: is it more important to minimize false positives or maximize true positives?
Precision aims to reduce false positives, whereas recall strives to maximize true positives.
Key Terms and Their Examples AKA the Confusion Table
1. True Positive (TP): This occurs when the model correctly predicts "Yes". Example: The model correctly identified that a photo contains a dog.
2. True Negative (TN): This occurs when the model correctly predicts "No". Example: The model correctly identified that a photo does not contain a dog (spoiler alert: it's a cat).
3. False Positive (FP) — Type 1 Error: This occurs when the model incorrectly predicts "Yes". Example: The model incorrectly identified a photo of a cat to be a dog.
4. False Negative (FN) — Type 2 Error: This occurs when the model incorrectly predicts "No". Example: The model incorrectly identified that a photo does not contain a dog, but it does.
