luokittelukokonaisuus
A luokittelukokonaisuus, a Finnish term, translates to "classification ensemble" in English. It refers to a machine learning technique where multiple classification models are combined to achieve better predictive performance than any single model could on its own. The core idea is to leverage the strengths of different algorithms and mitigate their weaknesses.
There are several common methods for constructing classification ensembles. One prominent approach is bagging, which involves
Another widely used ensemble method is boosting. In boosting, models are trained sequentially, with each new
Stacking, also known as stacked generalization, is a more complex ensemble technique. It involves training a
The advantages of using a luokittelukokonaisuus include improved accuracy, increased robustness, and reduced variance. By combining