Ensembling
Ensembling refers to the practice of combining multiple machine learning models to produce more accurate and stable predictions than any single model could achieve.
Background
The rationale is that different models capture different aspects of the data. By aggregating them, one can reduce overfitting, mitigate bias, and increase generalization. Ensemble methods are considered a cornerstone of modern applied machine learning.
Common approaches
- Bagging: training multiple models independently on resampled data (e.g., Random Forest).
- Boosting: sequentially building models that correct the mistakes of prior ones (e.g., Gradient Boosting, XGBoost).
- Stacking: combining predictions from multiple models via a meta-model.
Use cases
- Kaggle competitions: winning solutions often rely on complex ensembles.
- Fraud detection: integrating signals from different classifiers to improve detection rates.
- Healthcare: improving predictive accuracy in patient risk assessment.
Ensembling is often described as a way of turning a collection of “weak opinions” into a strong consensus. Even when individual models are not perfect, their combined output tends to be more robust, especially if they make different types of errors.
There are subtle trade-offs. While ensembles often boost accuracy, they also increase complexity: training and maintaining multiple models can be computationally expensive and harder to deploy in real-time systems. For this reason, in industry settings, ensembles are sometimes distilled into a single simpler model (through knowledge distillation) for production use.
Ensembling is not limited to classification. In regression, combining models can reduce variance in predictions. In reinforcement learning, ensembles help estimate uncertainty and improve exploration strategies. The central idea remains the same: diversity plus aggregation yields stability.
References
- Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. International Workshop on Multiple Classifier Systems.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
- Ensemble Learning : découvrez les modèles combinés, Innovatiana
