ensembleyhdistelmillä
Ensembleyhdistelmät, often translated as ensemble methods or combining models, is a technique in machine learning where multiple individual models are trained and then their predictions are combined to produce a final, often more accurate, prediction than any single model could achieve alone. This approach leverages the diversity of different models to reduce error and improve generalization.
The core idea behind ensembleyhdistelmät is that while individual models may have their own weaknesses and
Common methods for creating ensembleyhdistelmät include bagging (Bootstrap Aggregating) and boosting. Bagging involves training multiple instances
The effectiveness of ensembleyhdistelmät often depends on the diversity of the individual models. If all models