---
title: Comparison of Ensemble Methods in 2025
url: https://www.dataloco.com/en/comparison-of-ensemble-methods-in-2025
published: 2026-09-14T15:10:54+00:00
language: en
section: Data
source: https://machinelearningmastery.com/bagging-vs-boosting-vs-stacking-which-ensemble-method-wins-in-2025/
publisher: Dataloco
---

# Comparison of Ensemble Methods in 2025

A comparison of ensemble methods, specifically bagging, boosting, and stacking, was conducted to understand their effectiveness in 2025. This analysis reveals how each method operates and when to utilize them, with practical examples provided using Python.

Bagging, which stands for bootstrap aggregating, operates by training multiple models on different random subsets of data and averaging their predictions. The results indicate that on the iris dataset, vanilla bagging and random forests achieve identical mean cross-validation accuracy of 0.9667 ± 0.0211, yet their performance on a held-out test set differs, with scores of 0.9474 for bagging and 0.8947 for random forests.

Boosting is described as an ensemble technique that combines weak learners to create a robust predictive model. For the iris dataset, both AdaBoost and gradient boosting achieved a mean cross-validation score of 0.9600 ± 0.0327 and a test accuracy of 0.9737. Boosting techniques are noted for their ability to reduce bias through sequential error correction, making them effective on structured data where interactions are crucial.

Stacking, or stacked generalization, utilizes multiple base learners and a meta-learner to enhance prediction accuracy. The stacking classifier demonstrated a test accuracy of 0.9737 on the iris dataset, successfully integrating the strengths of random forest, gradient boosting, and support vector machines. The performance of stacking generally improves in larger datasets with diverse inductive biases.

Overall, the findings suggest that while random forests serve as strong baseline models, boosting is advantageous with proper regularization, and stacking can yield improvements when models differ in their errors. Various factors such as class imbalance and noise also influence the choice of method, emphasizing the importance of context in model selection.