Epidemiological data is often noisy and complex, making it challenging for single models to capture all underlying patterns accurately. Ensemble learning enhances model performance in several ways:
1. Improved Accuracy: By combining the predictions of multiple models, ensemble learning typically yields more accurate results than any single model. 2. Reduced Overfitting: This approach can help reduce overfitting, where a model performs well on training data but poorly on unseen data. 3. Robustness: Ensemble methods often provide more stable and reliable predictions, which is crucial in making public health decisions.
