Understanding Machine Learning Metrics 🤖
Machine learning metrics are vital for evaluating model performance. Some of the most important metrics include:
- Accuracy: Measures how often the classifier is correct.
- Precision: The number of true positive results divided by the number of all positive results.
- Recall: The number of true positive results divided by the number of positives that should have been retrieved.
- F1 Score: A balanced average of precision and recall.
- ROC-AUC: Represents the area under the ROC curve, indicating the ability of a model to distinguish between classes.
Choosing the right metrics helps you align them with business goals and ensures that models meet performance expectations.