Classification and regression models have distinct evaluation metrics, reflecting the differences in the nature of their predictions. Let’s compare the evaluation metrics commonly used for both types of models:
I. Classification Evaluation Metrics:
1. Accuracy:
- Definition: The ratio of correctly predicted instances to the total instances.
- Use Case: Suitable for balanced datasets.
- Considerations: May be misleading in the presence of imbalanced classes.
2. Precision:
- Definition: The ratio of true positive predictions to the sum of true positives and false positives.
- Use Case: Emphasizes the relevance of positive predictions.
- Considerations: May not be suitable if false negatives are critical.
3. Recall (Sensitivity or True Positive Rate):
- Definition: The ratio of true positive predictions to the sum of true positives and false negatives.
- Use Case: Emphasizes the ability to capture all positive instances.
