Accuracy is a metric used in binary classification to measure how often a model correctly classifies outputs. It is calculated using the formula:
Accuracy = (True Positives + True Negatives) / (True Positives + True Negatives + False Positives + False Negatives)
This formula evaluates the proportion of correct predictions (both positive and negative) out of all predictions made by the model.