Introduction to Precision and Recall
Precision and recall are metrics used to evaluate the performance of classification models. Precision measures the accuracy of positive predictions, while Recall (or True Positive Rate) measures the ability of a model to find all the relevant cases (or positive samples).
Why are Precision and Recall Important?
- Precision is vital when the cost of false positives is high, such as in medical diagnoses.
- Recall is crucial when it's important to capture all positive instances, like in search engines.
Explore Further
Explore more on how you can improve machine learning models: