Receiver operating characteristics (ROC curve): ROC curve illustrates the diagnostic ability of a binary classifier. It is calculated/created by plotting True Positive against False Positive at various threshold settings. The performance metric of ROC curve is AUC (area under curve). Higher the area under the curve, better the prediction power of the model.
In the context of machine learning, the ROC (Receiver Operating Characteristic) curve is a graphical representation that illustrates the performance of a binary classification model across different discrimination thresholds. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) for various threshold values.
Here’s a breakdown of the key terms related to the ROC curve:
- True Positive Rate (Sensitivity): The proportion of actual positive instances correctly predicted by the model. It is calculated as TP / (TP + FN), where TP is the number of true positives, and FN is the number of false negatives.
- False Positive Rate (1-Specificity): The proportion of actual negative instances incorrectly predicted as positive by the model. It is calculated as FP / (FP + TN), where FP is the number of false positives, and TN is the number of true negatives.
The ROC curve visually demonstrates the trade-off between sensitivity and specificity across different threshold values. A diagonal line (the line of no-discrimination) represents a random classifier, while a curve that is closer to the top-left corner indicates a better-performing model. The area under the ROC curve (AUC-ROC) is often used as a summary measure of the model’s performance, with a higher AUC indicating better discrimination.
In summary, the ROC curve provides valuable insights into the trade-offs between true positive rate and false positive rate, helping to assess and compare the performance of binary classification models.