Wisdom of the crowd — это принцип, согласно которому aggregation many independent judgments может дать более accurate result, чем reliance on a single judgment. В machine learning ensemble methods используют похожую идею, combining predictions from multiple models для повышения robustness и accuracy.
The idea that averaging the opinions or estimates of a large group of people ("the crowd") often produces surprisingly good results. For example, consider a game in which people guess the number of jelly beans packed into a large jar. Although most individual guesses will be inaccurate, the average of all the guesses has been empirically shown to be surprisingly close to the actual number of jelly beans in the jar. Ensembles are a software analog of wisdom of the crowd. Even if individual models make wildly inaccurate predictions, averaging the predictions of many models often generates surprisingly good predictions. For example, although an individual decision tree might make poor predictions, a decision forest often makes very good predictions.