[Feature Request]: Add Ensemble Learning in Machine Learning

[pavitraag]

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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

High

Record

• I have read the Contributing Guidelines
• I'm a GSSOC'24 contributor
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[github-actions]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[github-actions]

Hello @pavitraag! Your issue #3619 has been closed. Thank you for your contribution!