What does TPR stand for in the evaluation of machine learning models?

True Positive Rate (TPR) is a critical metric in the evaluation of machine learning models, particularly in the context of binary classification. It quantifies the proportion of actual positives that are correctly identified by the model, thereby reflecting its ability to detect positive instances.

TPR is calculated as the number of true positives divided by the sum of true positives and false negatives. This means it takes into account not just the successes in correctly identifying the positive cases but also the misses where the model failed to identify a positive when it actually existed. A high TPR indicates a model that is effective at capturing the positive cases, which is crucial in domains where overlooking positive instances can have significant consequences, such as in medical diagnosis or fraud detection.

Understanding TPR is vital for evaluating model performance, as it provides insights into sensitivity and helps in balancing trade-offs with other metrics, such as false positive rates, especially when optimizing for specific application needs. Conversations around metrics like precision and F1-score often involve TPR, illustrating its interconnectedness with overall model evaluation.

While "true predictive ratio," "true performance ratio," and "total predictive rate" may sound relevant, these terms do not represent widely accepted or standardized metrics in the context of machine learning evaluations. Thus,