When comparing ensembles to single models, key metrics like accuracy, precision, recall, and F1 score matter because ensembles aim to improve overall prediction quality. Ensembles reduce errors by combining multiple models, so metrics that reflect error reduction and balanced performance (like F1 score) best show their advantage.
Single Model Confusion Matrix: TP=80 FP=20 FN=30 TN=70 Ensemble Model Confusion Matrix: TP=90 FP=15 FN=20 TN=75 Total samples = 200 for both Note: Ensemble has fewer false negatives and false positives, improving precision and recall.
Single models may have higher false positives or false negatives. Ensembles balance this by combining predictions, reducing both errors.
Example: For spam detection, a single model might catch most spam (high recall) but mark many good emails as spam (low precision). An ensemble can reduce false spam flags, improving precision without losing recall.
Good ensemble metrics: Higher accuracy, precision, recall, and F1 score than single models. For example, precision and recall above 0.85 show balanced, reliable predictions.
Bad ensemble metrics: Similar or worse than single models, indicating poor combination or overfitting.
Your ensemble model has 95% accuracy but 50% recall on the positive class. Is it good for detecting rare events? No, because low recall means many positives are missed, which is critical in rare event detection.