Techniques that combine multiple individual models to produce a single, more accurate and robust prediction — leveraging the principle that a group of diverse models outperforms any single model alone.
In Depth
Ensemble methods are based on a principle known as the 'wisdom of crowds' — combining the predictions of multiple diverse models often yields better results than relying on any single model. Each individual model may make different errors, but when their predictions are aggregated (through voting, averaging, or more sophisticated combination strategies), the errors tend to cancel out. This reduces variance, can reduce bias, and typically produces more robust, generalizable predictions.
The three main ensemble strategies are bagging, boosting, and stacking. Bagging (Bootstrap Aggregating) trains multiple instances of the same algorithm on random subsets of the data and averages their predictions — Random Forest is the most famous bagging method. Boosting trains models sequentially, where each new model focuses on correcting the mistakes of its predecessors — XGBoost, LightGBM, and AdaBoost are prominent boosting methods. Stacking trains a meta-model that learns the optimal way to combine the predictions of several diverse base models.
Ensemble methods consistently achieve top performance in machine learning benchmarks and competitions. On Kaggle, virtually every winning solution for tabular data uses some form of ensembling. However, ensembles come with tradeoffs: they increase computational cost, reduce interpretability, and add engineering complexity. In production systems, the marginal accuracy gain from ensembling must be weighed against latency, memory, and maintenance requirements.
Ensemble methods combine multiple models to reduce errors and improve robustness — a strategy that dominates competitive machine learning and powers many production AI systems.