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2 months ago 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!
Is there an existing issue for this?
Feature Description
Integrate the AdaBoost (Adaptive Boosting) algorithm into the project to enhance its classification and regression capabilities. AdaBoost is a powerful ensemble learning technique that combines multiple weak learners (typically decision trees) to create a strong predictive model. This feature should include options for setting the number of estimators, learning rate, and base estimator parameters, allowing users to fine-tune the model to their specific needs.
Use Case
In real-life applications, AdaBoost can be utilized to improve the accuracy and robustness of predictive models in various domains. For instance, in medical diagnostics, AdaBoost can help in building models that accurately classify diseases based on patient data, leading to better diagnosis and treatment plans. In the finance sector, AdaBoost can be used to detect fraudulent transactions by combining multiple weak classifiers to improve detection rates. By leveraging the strengths of AdaBoost, users can achieve higher prediction accuracy and make more informed decisions.
Priority
High
Record