Understanding Precision and Recall
Welcome to our comprehensive guide on precision and recall, two crucial metrics used to evaluate the performance of binary classification models in machine learning.
Precision
Precision is the ratio of correctly predicted positive observations to the total predicted positives. It essentially answers the question: What proportion of positive identifications was actually correct?
- Formula: Precision = TP / (TP + FP)
- Where TP is True Positives and FP is False Positives.
Recall
Recall (also known as sensitivity) is the ratio of correctly predicted positive observations to the all observations in actual class. It answers the question: What proportion of actual positives was identified correctly?
- Formula: Recall = TP / (TP + FN)
- Where FN is False Negatives.
Balancing Precision and Recall
Often, there is a trade-off between precision and recall. Increasing one usually decreases the other. Techniques such as F1 Score can help balance these metrics.
Illustration
Further Reading
For more detailed exploration, check out our guide on Machine Learning Metrics or visit the Wikipedia page on Precision and Recall.