Ensemble methods combine several models so that their joint prediction beats any individual member. The logic is the wisdom of crowds: models that make different mistakes can cancel out one another's errors, provided their outputs are aggregated sensibly. Classic techniques include bagging, training models on different samples of the data and averaging them, as in random forests; boosting, training models sequentially so each corrects its predecessors' errors, as in XGBoost and LightGBM; and stacking, where a meta-model learns how best to weigh the base models' outputs.
Ensembles remain the workhorses of tabular machine learning, credit scoring, demand forecasting, churn prediction, where gradient-boosted ensembles still routinely outperform deep learning. The idea has also migrated into the LLM era in new forms: sampling a model several times and taking the majority answer, known as self-consistency, routing queries among models with different strengths, or using one model to check another's output. The trade-offs are consistent across all forms: better accuracy and robustness in exchange for higher inference cost and reduced interpretability, since explaining a committee is harder than explaining one member.
At arosplatforms we use ensembles where the economics justify them. For high-stakes predictive workloads we deploy boosted ensembles with explainability tooling layered on top, and in LLM systems we apply ensemble thinking selectively, majority voting or a second-model check on decisions where an error is expensive, while keeping cheap single-pass inference for everything routine.