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About Ensemble Learning
Ensemble learning is a process that combines multiple machine learning models to improve performance. It is commonly used to reduce the risk of selecting a single poor-performing model. Ensemble methods generate diverse models by training them on different subsets of data or using different parameters. Their individual decisions are then combined, such as through majority voting, to make an overall prediction. This approach can learn complex decision boundaries that a single model cannot, effectively dividing problems into simpler sub-problems that are solved individually. It also allows assigning confidence levels to predictions based on the agreement of models within the ensemble.
Detailed Information
| Author: | ['dfnngfff'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 21 |
| Language: | English |
| Format: | |
| Price: | FREE |
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