Which metric is commonly used to evaluate the performance of a classification model?

Which metric is commonly used to evaluate the performance of a classification model?

• A. Root Mean Square Error
• B. Accuracy
• C. Silhouette Score
• D. Mean Absolute Error

Answer: (B)

Accuracy is a widely used metric for evaluating the performance of a classification model. It represents the proportion of correctly predicted instances (true positives and true negatives) out of the total instances examined. This measure effectively provides a straightforward assessment of how well the model is performing across all classes in the dataset. In scenarios where the classes are balanced, accuracy can be a reliable indicator of model effectiveness. However, it becomes crucial to consider the context of the problem as well as the distribution of the classes since accuracy alone may not give an adequate picture in imbalanced datasets. Other metrics like Root Mean Square Error, Silhouette Score, and Mean Absolute Error are generally associated with regression tasks or clustering evaluations, respectively. Thus, they are not suitable choices when determining the performance of a classification model.

Accuracy is a widely used metric for evaluating the performance of a classification model. It represents the proportion of correctly predicted instances (true positives and true negatives) out of the total instances examined. This measure effectively provides a straightforward assessment of how well the model is performing across all classes in the dataset.

In scenarios where the classes are balanced, accuracy can be a reliable indicator of model effectiveness. However, it becomes crucial to consider the context of the problem as well as the distribution of the classes since accuracy alone may not give an adequate picture in imbalanced datasets.

Other metrics like Root Mean Square Error, Silhouette Score, and Mean Absolute Error are generally associated with regression tasks or clustering evaluations, respectively. Thus, they are not suitable choices when determining the performance of a classification model.