Which metric measures overall accuracy across all predictions?

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Multiple Choice

Which metric measures overall accuracy across all predictions?

Measuring overall correctness across all predictions is done with accuracy. It represents the proportion of predictions the model got right out of all predictions, calculated as (true positives plus true negatives) divided by the total number of predictions. This single metric captures how well the model performs on both classes, giving a complete snapshot of overall performance. Precision, recall, and F1 look at different aspects. Precision focuses on the correctness of positive predictions, recall on how many actual positives were found, and F1 balances precision and recall. These don't reflect overall accuracy across every prediction, especially when negative cases or positive cases dominate or when you care about overall correctness rather than performance on a specific class. So, for a single, broad measure of how often the model is right across the entire set of predictions, accuracy is the best fit.

Measuring overall correctness across all predictions is done with accuracy. It represents the proportion of predictions the model got right out of all predictions, calculated as (true positives plus true negatives) divided by the total number of predictions. This single metric captures how well the model performs on both classes, giving a complete snapshot of overall performance.

Precision, recall, and F1 look at different aspects. Precision focuses on the correctness of positive predictions, recall on how many actual positives were found, and F1 balances precision and recall. These don't reflect overall accuracy across every prediction, especially when negative cases or positive cases dominate or when you care about overall correctness rather than performance on a specific class.

So, for a single, broad measure of how often the model is right across the entire set of predictions, accuracy is the best fit.

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