Added a powerful new tool to the Parkinson's disease prediction model… #83
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…: 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
approved these changes
Jun 1, 2024
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
…: 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%.