Key metrics to assess the performance of a predictive model include:
- Accuracy: The proportion of correct predictions out of all predictions. - Precision and Recall: Precision measures the accuracy of positive predictions, while recall measures the ability to identify all relevant instances. - F1 Score: The harmonic mean of precision and recall, providing a balanced measure. - ROC-AUC: The area under the receiver operating characteristic curve, indicating the model's ability to distinguish between classes.