The law of large numbers states that as the number of trials or observations increases, the sample mean will converge to the expected value or population mean. This concept is crucial in understanding how ensemble methods like bagging and random forests work, as it highlights the benefits of averaging predictions from multiple models to improve accuracy and reduce variance.
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