Rodney Brooks
Machine Learning
A graph showing the trade-off between true positive rate and false positive rate at various classification thresholds.
Detailed Explanation
The ROC Curve (Receiver Operating Characteristic Curve) visually represents a classifier's performance by plotting the true positive rate (sensitivity) against the false positive rate at different threshold settings. It helps assess the model's ability to distinguish between classes; a curve closer to the top-left indicates better performance, with the area under the curve (AUC) summarizing overall accuracy.
Use Cases
- •Evaluates model performance by comparing true positive rates to false positive rates across thresholds, optimizing classifier selection for better accuracy.
Related Terms
Other terms in the Machine Learning category