Which metric measures overall correctness as correct predictions divided by total samples?

Which metric measures overall correctness as correct predictions divided by total samples?

• A. Accuracy
• B. Precision
• C. Recall
• D. F-score

Answer: (A)

Measuring overall correctness of predictions. Accuracy is the proportion of correct predictions out of all predictions, calculated as (true positives plus true negatives) divided by (true positives plus true negatives plus false positives plus false negatives). This directly reflects how often the model gets things right across the entire dataset, which is why it’s the best choice for assessing overall correctness. Other metrics focus on specific aspects: precision looks at correctness of positive predictions, recall at how many actual positives were found, and the F-score combines precision and recall. Keep in mind that accuracy can be misleading if the data are imbalanced, since a model could be correct most of the time simply by predicting the majority class.

Measuring overall correctness of predictions. Accuracy is the proportion of correct predictions out of all predictions, calculated as (true positives plus true negatives) divided by (true positives plus true negatives plus false positives plus false negatives). This directly reflects how often the model gets things right across the entire dataset, which is why it’s the best choice for assessing overall correctness. Other metrics focus on specific aspects: precision looks at correctness of positive predictions, recall at how many actual positives were found, and the F-score combines precision and recall. Keep in mind that accuracy can be misleading if the data are imbalanced, since a model could be correct most of the time simply by predicting the majority class.