Machine learning metrics are essential for evaluating the performance of models. These metrics help us understand how well our models are performing and where we might need to improve.
Common Metrics
- Accuracy: The ratio of correctly predicted observations to the total observations. Formula: ( \text{Accuracy} = \frac{\text{True Positives} + \text{True Negatives}}{\text{Total Observations}} )
- Precision: The ratio of correctly predicted positive observations to the total predicted positives. Formula: ( \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}} )
- Recall: The ratio of correctly predicted positive observations to all actual positives. Formula: ( \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} )
- F1 Score: The weighted average of Precision and Recall. Formula: ( \text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} )
Useful Links
Machine Learning Metrics