What term describes the ratio of correct predictions to total predictions made in a model?

The term that describes the ratio of correct predictions to total predictions made by a model is accuracy. Accuracy is a fundamental performance metric used to evaluate the effectiveness of a classification model. It is calculated by taking the number of truly correct predictions (both true positives and true negatives) and dividing it by the total number of predictions made (the sum of true positives, true negatives, false positives, and false negatives).

This metric provides a straightforward measure of how often the model is correct across all classes. It is most useful in situations where the classes are balanced, meaning that the number of instances in each class is roughly equal.

In contrast, precision measures the number of true positive predictions made out of all positive predictions, meaning it focuses specifically on the relevance of positive predictions. Recall, on the other hand, evaluates how many actual positive cases were correctly identified by the model, focusing on capturing all positives. The F1 Score combines both precision and recall into a single metric that gives a better understanding of a model's performance, particularly with imbalanced datasets. However, accuracy remains the most direct and commonly used metric for assessing overall prediction correctness.