[Feature]: <Ensemble Methods>

[priyashuu]

Feature Name

Adding Ensemble MethodsVisualizations

Feature Description

Introduce ensemble methods such as bagging, boosting, and stacking. These techniques combine multiple models to improve performance, increase robustness, and reduce the risk of overfitting.

Bagging: This technique trains multiple instances of the same model on different subsets of the training data, usually through bootstrapping. The final prediction is made by averaging (for regression) or voting (for classification).

Boosting: This sequential ensemble technique focuses on correcting errors made by previous models. Each new model is trained on the residuals of the combined ensemble's predictions, leading to improved performance.

Stacking: This method combines different models (which could be of varying types) and uses a meta-learner to make the final prediction based on the outputs of the base models.

Motivation

Ensemble methods are widely recognized for their ability to enhance model accuracy and reliability. By implementing these techniques, users will have access to more powerful modeling strategies that can significantly improve predictive performance on complex datasets.

Implementation Suggestions (Optional)

No response

Feature Type

New Algorithm

Does this feature require additional resources?

• [X] Additional Documentation
• [ ] Third-Party Libraries
• [ ] UI/UX Design
• [ ] Other

References (Optional)

No response

[github-actions[bot]]

👋 Hi @priyashuu! Thanks for opening this issue. We appreciate your contribution to the Algo project. Our team will review it soon.

[priyashuu]

please assign this issue to me @ajay-dhangar