What is the formula for calculating accuracy in a classification model?

The formula for calculating accuracy in a classification model is indeed represented by the first choice: (TP + TN) / (TP + TN + FP + FN). This formula quantifies how well a classification model is performing by measuring the proportion of correct predictions—both true positives (TP) and true negatives (TN)—against the total number of predictions made, which includes false positives (FP) and false negatives (FN) as well.

In this formula:

• True Positives (TP) refer to instances where the model correctly predicts the positive class.

• True Negatives (TN) denote instances where the model correctly predicts the negative class.

• False Positives (FP) are cases where the model incorrectly predicts the positive class when it is actually negative.

• False Negatives (FN) are instances where the model incorrectly predicts the negative class when it is actually positive.

The combination of TP and TN in the numerator allows us to capture all correct predictions, providing a clear measure of accuracy as a fraction of total predictions.

The other options do not accurately represent the standard calculation for accuracy in classification models. For instance, the second option calculates precision, which focuses only on the correctness of positive predictions, while the third option does not pertain to accuracy but