Evaluating the model is critical to ensure it performs well. Common metrics include:
Accuracy - The percentage of correct predictions. Precision - The ratio of true positive predictions to the total predicted positives. Recall - The ratio of true positive predictions to the actual positives. F1-Score - The harmonic mean of precision and recall. AUC-ROC Curve - Measures the model's ability to distinguish between classes.