aggregointimenetelmistä
Aggregointimenetelmistä, or aggregation methods, refers to a class of machine learning techniques that combine the predictions of multiple individual models to produce a more robust and accurate final prediction. The core idea is that by leveraging the strengths of diverse models and mitigating their weaknesses, the aggregated output can often outperform any single model used in isolation. This approach is widely applicable in various fields, including classification, regression, and forecasting.
A common type of aggregation is bagging, which stands for bootstrap aggregating. Bagging involves creating multiple
Another prominent technique is boosting. Unlike bagging, boosting trains models sequentially. Each subsequent model focuses on
Stacking, also known as stacked generalization, is a more complex aggregation method. It involves training a
These aggregation methods are powerful tools for improving predictive performance and reducing variance, making them essential