What type of model evaluation metric is commonly used for classification tasks?

In classification tasks, accuracy is a widely used evaluation metric because it provides a straightforward assessment of how well a model is performing. Accuracy measures the proportion of correctly predicted instances (both true positives and true negatives) out of the total instances examined. This metric is particularly useful when the classes are balanced, as it gives an immediate sense of overall performance.

When analyzing classification models, accuracy helps to quickly identify the percentage of correct predictions, which can be helpful in both model selection and assessment stages. This metric is often expressed as a percentage, making it intuitive and easy to understand.

While other metrics such as precision, recall, and F1-score are also relevant for classification tasks, especially in cases of imbalanced datasets where accuracy alone may not provide a true representation of performance, accuracy remains a fundamental starting point for evaluating model efficacy.

Mean Squared Error and R-squared are metrics typically associated with regression tasks, focusing on the differences between predicted and actual continuous values rather than categorical classifications. Thus, they would not typically apply when assessing classification models.