Ensemble learning enhances predictive accuracy by combining multiple models to mitigate bias and improve generalisation. Used in fields like healthcare, traffic management, and economic policy, it ensures reliable decision-making through techniques like bagging, boosting, and stacking.
Ensemble learning combines predictions from multiple models to improve accuracy, reduce overfitting, and handle complex data patterns. This blog explores its history, methods, and applications in Australian sectors like healthcare, traffic management, and education analytics.
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