I came through this statement in a Machine Learning text book based on law of large numbers:
Suppose you build an ensemble containing 1,000 classifiers that are individually correct only 51% of the time (barely better than random guessing). If you predict the majority voted class, you can hope for up to 75% accuracy!
I understand the analogy if we consider average over 1000 predictions but how majority votes lead to 75% accuracy from 51% (individual)?