What ROC Curve Meaning, Applications & Example
Plot of true positive rate versus false positive rate.
What is a ROC Curve?
A ROC Curve (Receiver Operating Characteristic Curve) is a graphical representation of a classification model ’s performance at various thresholds. It plots the True Positive Rate (Recall) against the False Positive Rate for different classification thresholds, showing how well the model discriminates between classes.
Components of the ROC Curve
- True Positive Rate (TPR): Also known as recall , it is the proportion of actual positives correctly identified.
- False Positive Rate (FPR): The proportion of actual negatives incorrectly classified as positive.
The curve helps in evaluating a model’s ability to balance between sensitivity and specificity.
AUC (Area Under the Curve)
- AUC: The area under the ROC curve (AUC) is a summary statistic that quantifies the overall ability of the model to discriminate between positive and negative classes. A higher AUC (close to 1) indicates better performance.
Applications of the ROC Curve
- Medical Testing: Used to evaluate the ability of diagnostic tests to distinguish between healthy and diseased individuals.
- Fraud Detection: Assesses the ability of fraud detection systems to distinguish between fraudulent and legitimate transactions.
- Spam Filters: Evaluates the performance of email classifiers distinguishing spam from non-spam messages.
Example of ROC Curve
In a disease detection scenario, if a model is tested at different probability thresholds, the ROC curve shows how the true positives and false positives change as the threshold varies. A model with a high AUC will have a curve that is closer to the top-left corner of the plot, indicating better classification performance.