Ensemble Learning
Ensemble learning is a machine learning approach that combines predictions from multiple models to improve accuracy, robustness, and generalization. Introduced in the 1990s, this technique leverages the diversity of base learners—such as decision trees, neural networks, or support vector machines—to reduce errors and variances. The fundamental principle of ensemble learning is that combining multiple models generally leads to better predictive performance than any individual model, provided the individual models are diverse and accurate to some degree.
https://en.wikipedia.org/wiki/Ensemble_learning
There are three main categories of ensemble learning methods: bagging, boosting, and stacking. Bagging, or bootstrap aggregating, involves training multiple independent models on different subsets of the data and combining their outputs, as seen in methods like Random Forest. Boosting sequentially trains models, where each subsequent model corrects the errors of the previous one, as implemented in AdaBoost and Gradient Boosting Machines. Stacking combines predictions from multiple models using a meta-model, creating a layered architecture of predictions. Each of these approaches excels in different scenarios depending on the complexity and type of the dataset.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble learning is widely applied in domains such as finance, healthcare, and image recognition, owing to its high accuracy and ability to mitigate overfitting. Techniques like bagging enhance stability, while boosting improves bias handling. However, ensemble methods often require more computational resources due to the training of multiple models, and interpretability can be challenging as the complexity of the ensemble increases. Despite these drawbacks, ensemble learning remains a cornerstone of modern machine learning frameworks.
https://towardsdatascience.com/ensemble-learning-3213dc03bfb7