Combining multiple forecasts into a single, often more reliable, estimate reduces individual model bias and random error. This technique, widely used in weather forecasting, finance, and machine learning, improves accuracy by balancing different perspectives. Data scientists, traders, and meteorologists benefit from more stable predictions, leading to better decision-making and reduced risk.
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