yhdistelmämallien

Yhdistelmämallit, known in English as ensemble models or ensemble methods, are a machine learning technique where multiple individual models are trained to solve the same problem, and their predictions are combined to produce a final output. The core idea behind ensemble methods is that by combining the strengths of several weaker models, a stronger, more robust, and often more accurate model can be created. This is analogous to seeking advice from multiple experts rather than just one, as their diverse perspectives can lead to a more well-rounded decision.

There are several common strategies for creating ensemble models. Bagging, such as Random Forests, involves training multiple models on different random subsets of the training data, often with replacement. Boosting, exemplified by algorithms like AdaBoost and Gradient Boosting, sequentially trains models, with each subsequent model focusing on correcting the errors made by the previous ones. Stacking, or stacked generalization, takes a different approach by training a meta-model to learn how to best combine the predictions of several base models.

Ensemble methods are widely used due to their ability to improve predictive performance, reduce overfitting, and increase model stability. They are particularly effective when the individual models in the ensemble are diverse, meaning they make different types of errors. This diversity can be achieved through various means, such as using different algorithms for the base models or training the same algorithm on different subsets of data or with different hyperparameters. The final prediction from an ensemble can be determined by averaging predictions (for regression tasks) or by majority voting (for classification tasks), or more sophisticated methods as in stacking.