Ensemble Learning

The process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence program.

How it works

Ensemble methods combine the predictions of multiple base models to produce a final output. Bagging (e.g., Random Forests) trains many models on different random subsets of the data and averages their predictions, reducing variance. Boosting (e.g., XGBoost, LightGBM) trains models sequentially, with each new model focusing on the errors of the previous ones, reducing bias. Stacking trains a meta-model on the outputs of base models to learn an optimal combination strategy.

Why it matters

Individual models are prone to their own specific errors and biases, but these errors tend to be uncorrelated across different models. By combining many models, their individual mistakes cancel out and their collective wisdom prevails. Ensemble methods consistently outperform single models in competitions and industry applications, particularly on structured tabular data. The Mixture of Experts (MoE) architecture used in frontier LLMs like DeepSeek-V3 is a learned form of ensemble routing.