Which graph evaluates classifier performance by plotting the true positive rate against the false positive rate at various thresholds?

The Receiver Operating Characteristic (ROC) Curve is the correct choice because it specifically visualizes the trade-off between the true positive rate (sensitivity) and the false positive rate (1 - specificity) at various threshold settings for a binary classifier. This graph provides a comprehensive overview of a classifier’s performance across all classification thresholds, allowing users to assess how changes in the threshold affect the rates of positive and negative classifications.

The ROC Curve is particularly useful in scenarios where you have imbalanced datasets, as it focuses on the sensitivity and specificity of the classifier without being influenced by class distribution. It provides a powerful way to visualize the performance and can help in selecting the optimal threshold for making decisions based on the classification probabilities. The area under the ROC Curve (AUC) quantifies the overall performance of the classifier; a higher AUC indicates better performance.

In contrast, the Lift Chart measures the gain or lift achieved by using the predictive model rather than random guessing, focusing on the predicted positives. The Precision-Recall Curve, while also evaluating classifier performance, concentrates more on the trade-off between precision and recall (sensitivity) rather than the broader perspective provided by the ROC. The Confusion Matrix is a summary table used to display the performance of a classification