Ensemble learning gives credence to the idea of the “wisdom of crowds,” which suggests that the decision-making of a larger group of people is typically better than that of an individual expert. Similarly, ensemble learning refers to a group (or ensemble) of base learners, or models, which work collectively to achieve a better final prediction.
A single model, also known as a base or weak learner, may not perform well individually due to high variance or high bias. However, when weak learners are aggregated, they can form a strong learner, as their combination reduces bias or variance, yielding better model performance.
Ensemble methods frequently use decision trees for illustration. This algorithm can be prone to overfitting, showing high variance and low bias, when it hasn’t been pruned. Conversely, it can also lend itself to underfitting, with low variance and high bias, when it’s very small, like a decision stump, which is a decision tree with one level.
Remember, when an algorithm overfits or underfits to its training set, it cannot generalize well to new data sets, so ensemble methods are used to counteract this behavior to allow for generalization of the model to new data sets. While decision trees can exhibit high variance or high bias, it’s worth noting that it is not the only modeling technique that leverages ensemble learning to find the “sweet spot” within the bias-variance tradeoff.