La sabiduría de la multitud es el principio de que agregar muchos juicios independientes puede producir un resultado más preciso que depender de un único juicio. En aprendizaje automático, los métodos de ensemble usan una idea similar al combinar predicciones de múltiples modelos para mejorar robustez y exactitud.
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.