How is accuracy in predictive models typically calculated?

Accuracy in predictive models is a fundamental metric that assesses how well a model performs in making correct predictions. It is calculated by taking the number of correct predictions and dividing it by the total number of predictions made. This formula provides a straightforward measure of performance, reflecting the proportion of accurate results out of all attempts.

The rationale for this method is that it encapsulates both true positives and true negatives in the context of classification tasks. By focusing on the correct predictions relative to all predictions, accuracy gives a clear overview of the model's effectiveness in predicting outcomes correctly across the entire dataset.

In contrast, other options discuss aspects that do not accurately define how accuracy is calculated. For instance, merely counting the total number of predictions does not provide insight into correctness, and focusing solely on incorrect predictions fails to consider the correct predictions that are crucial for calculating accuracy. Additionally, averaging true positive and false positive rates pertains to metrics like precision and recall rather than defining overall accuracy.