When is an ensemble model observed to be more accurate?

An ensemble model tends to be more accurate when individual models disagree because this disagreement often indicates that the models capture different aspects of the underlying data. By combining models that provide varying predictions, the ensemble can leverage the strengths of each model while mitigating their individual weaknesses. This diversity among the models contributes to a more robust overall prediction, resulting in improved accuracy.

When there is disagreement, it suggests that each model may be accounting for different patterns or relationships in the dataset. This characteristic allows the ensemble to create a more nuanced and comprehensive understanding of the data, as each model brings its unique insights to the collective prediction.

In contrast, if individual models agree too closely, they may be reinforcing the same biases or errors, which could lead to less variability in predictions and, consequently, lower overall accuracy. Therefore, having models that offer differing predictions is a critical ingredient in maximizing the effectiveness of ensemble techniques.