Description: The True Positive Rate (TPR) is a fundamental metric in the field of anomaly detection using artificial intelligence and machine learning. It is defined as the proportion of actual positive cases that are correctly identified by a detection model. In other words, it measures the effectiveness of a system in accurately recognizing instances that are truly anomalous. This rate is expressed as a percentage and is calculated by dividing the number of true positives (correctly identified cases) by the sum of true positives and false negatives (cases that are positive but were not identified). TPR is crucial in various applications where precise identification of anomalies is vital, such as fraud detection, health monitoring systems, and cybersecurity. A high true positive rate indicates that the model is effective in its task, translating into greater confidence in decisions based on its results. However, it is important to balance this metric with others, such as the false positive rate, to obtain a comprehensive view of the model’s performance. In summary, the True Positive Rate is a key indicator that helps assess the accuracy and reliability of AI-driven anomaly detection systems.
