yhdistelmäoppiminen

Yhdistelmäoppiminen, known in English as Ensemble Learning, is a machine learning paradigm where multiple learning algorithms are combined to achieve better predictive performance than any single constituent algorithm could achieve alone. The core idea is to leverage the strengths of diverse models and mitigate their weaknesses through aggregation.

Different types of ensemble methods exist, broadly categorized into sequential and parallel approaches. Sequential methods, such as Boosting, train models iteratively, with each subsequent model focusing on correcting the errors made by the previous ones. Examples include AdaBoost and Gradient Boosting. Parallel methods, like Bagging, train multiple models independently and then combine their predictions, typically through voting or averaging. Random Forests, a popular example, are built using bagging and feature randomness. Stacking is another approach where a meta-learner is trained to combine the predictions of base learners.

The effectiveness of ensemble learning stems from reducing variance, bias, or both. By combining predictions from multiple models, particularly if they are diverse and not highly correlated, the overall error can be significantly reduced. Ensemble methods are widely used in various applications, including classification, regression, and feature selection, often yielding state-of-the-art results. The choice of ensemble method and the base learners depends on the specific problem and data characteristics.