Artificial Intelligence & Machine Learning
ROC Curve
Definition
A Receiver Operating Characteristic (ROC) curve is a graph showing the performance of a classification model at all classification thresholds. It plots the True Positive Rate (Recall) against the False Positive Rate.
Why It Matters
The ROC curve helps to visualize the trade-off between sensitivity (recall) and specificity (1 - FPR). It provides a comprehensive view of a model's performance across all possible thresholds.
Contextual Example
A model outputs a probability score. If you set the threshold at 0.5, you get a certain number of true positives and false positives. The ROC curve shows the performance you would get at all other thresholds (0.6, 0.7, etc.).
Common Misunderstandings
- The Area Under the Curve (AUC) is a metric that summarizes the entire ROC curve into a single number. An AUC of 1.0 represents a perfect model, while 0.5 represents a model with no better than random chance.
- It is a very common way to compare the performance of different classification models.