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Feature Description
Ensemble Learning is a technique that combines multiple base models to produce a more robust and accurate predictive model. By aggregating the predictions of several models, ensemble methods, such as bagging, boosting, and stacking, can improve generalization and reduce the risk of overfitting compared to individual models.
Use Case
In a real-time use case, a financial analyst predicting stock market trends can use an ensemble of models like Random Forest, Gradient Boosting, and Support Vector Machines. By combining these models, the analyst can achieve more accurate and stable predictions, enhancing investment strategies and decision-making processes in a highly volatile market.
Benefits
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Priority
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