When evaluating classification problems, the most suitable metrics often include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC). Among the options provided, the most appropriate metric is:

### Accuracy
**Reasoning:**
- **Accuracy** measures the proportion of correct predictions made by the model out of all predictions. It is particularly useful when the classes are balanced, meaning the number of instances of each class is roughly the same.

The other metrics listed (Mean Squared Error, R-squared, and Mean Absolute Error) are generally used for regression problems rather than classification, as they deal with continuous output rather than categorical labels.

### Summary
In conclusion, for classification problems, the suitable evaluation metric from the given options is **Accuracy**.

Question

When evaluating classification problems, the most suitable metrics often include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC). Among the options provided, the most appropriate metric is:

### Accuracy
**Reasoning:**
- **Accuracy** measures the proportion of correct predictions made by the model out of all predictions. It is particularly useful when the classes are balanced, meaning the number of instances of each class is roughly the same.
  
The other metrics listed (Mean Squared Error, R-squared, and Mean Absolute Error) are generally used for regression problems rather than classification, as they deal with continuous output rather than categorical labels.

### Summary
In conclusion, for classification problems, the suitable evaluation metric from the given options is **Accuracy**.

Knowee AI · Accepted Answer

When evaluating classification problems, the most suitable metrics often include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC). Among the options provided, the most appropriate metric is:

### Accuracy
**Reasoning:**
- **Accuracy** measures the proportion of correct predictions made by the model out of all predictions. It is particularly useful when the classes are balanced, meaning the number of instances of each class is roughly the same.
  
The other metrics listed (Mean Squared Error, R-squared, and Mean Absolute Error) are generally used for regression problems rather than classification, as they deal with continuous output rather than categorical labels.

### Summary
In conclusion, for classification problems, the suitable evaluation metric from the given options is **Accuracy**.

hich evaluation metric is suitable for classification problems?Answer areaMean Squared ErrorR-squaredAccuracyMean Absolute Error

Question

Which evaluation metric is suitable for classification problems?

Solution

Accuracy

Summary

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