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Added a powerful new tool to the Parkinson's disease prediction model... #83
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...: Gradient Boosting. This method boosts accuracy by combining predictions from multiple models. I specifically chose XGBoost for its efficiency and strong performance in machine learning. The Gradient Boosting algorithm, specifically XGBoost, has been added to the model pipeline for Parkinson's disease detection. This algorithm leverages the power of ensemble learning by sequentially building a series of decision trees, where each tree corrects the errors of the previous ones. The model finally gives the accuracy of 94%.
Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊
sanjay-kv
sanjay-kv
approved these changes
Jun 1, 2024
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...: Gradient Boosting. This method boosts accuracy by combining predictions from multiple models. I specifically chose XGBoost for its efficiency and strong performance in machine learning.
The Gradient Boosting algorithm, specifically XGBoost, has been added to the model pipeline for Parkinson's disease detection. This algorithm leverages the power of ensemble learning by sequentially building a series of decision trees, where each tree corrects the errors of the previous ones. The model finally gives the accuracy of 94%.