Ensemble learning is a machine learning technique that combines the predictions of multiple individual models (often called 'weak learners') to produce a single, stronger predictive model. The core idea is that by aggregating the 'votes' of several models, the final prediction will be more accurate and robust than any of the individual model's predictions alone. Common ensemble methods include bagging (e.g., Random Forests), boosting (e.g., AdaBoost, Gradient Boosting), and stacking. Ensembles are effective because they can reduce both bias and variance, leading to improved generalization performance on new, unseen data.
The theoretical foundations of ensemble methods trace back to early work on combining classifiers in the 1970s and 80s. The modern era was largely kicked off by the development of bagging (Bootstrap Aggregating) by Leo Breiman in 1996 and boosting algorithms like AdaBoost by Yoav Freund and Robert Schapire in 1997. These methods provided practical and powerful ways to improve the accuracy of machine learning models, particularly decision trees. The concept is analogous to the 'wisdom of crowds,' where a collective opinion is often better than a single expert's.
Ensemble methods have become a dominant force in both academic research and applied machine learning. They are consistently among the top-performing methods in data science competitions like Kaggle. Gradient Boosting Machines (GBMs), such as XGBoost, LightGBM, and CatBoost, are especially popular and have become a standard tool for working with tabular data. The success of these methods has led to their integration into all major machine learning libraries, including scikit-learn, making them widely accessible.