Classifier Performance Evaluation: Accuracy, Sensitivity & ROC Curve

This report delves into the evaluation of classifier performance, emphasizing the significance of accuracy, sensitivity, and specificity metrics. It explains how these metrics, derived from true positives, false positives, true negatives, and false negatives, help in assessing the effectiveness of a classifier. The report highlights the limitations of relying solely on sensitivity and introduces the Receiver Operating Characteristic (ROC) curve as a solution to overcome these limitations. By plotting sensitivity against (1-specificity) for various thresholds, the ROC curve provides a comprehensive view of a classifier's performance, with the area under the curve (AUC) indicating its overall effectiveness. The document references external sources to support its explanations and provides a practical example with calculations to illustrate the application of these metrics in real-world scenarios. Desklib provides this and other solved assignments for students.