💡[Feature]: Adding Diabetes detection model using 3 ensemble models #1318
Description
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Feature Description
Diabetes detection model using 3 ensemble models involves building a machine learning system to detect diabetes using the Pima Indians Diabetes dataset from Kaggle. The system incorporates three ensemble models:
- Stacking model: Combines multiple classifiers and uses another model to aggregate the predictions.
- Soft voting model: Takes the weighted average of predicted probabilities from different models.
- Hard-soft voting model: Incorporates both hard voting (majority class prediction) and soft voting (probability-based averaging) strategies.
This approach aims to enhance prediction accuracy through ensemble learning techniques.
Use Case
This feature would enhance the project's predictive accuracy for diabetes detection by leveraging multiple ensemble models, offering more reliable and robust predictions. It would also improve decision-making in healthcare applications by combining different models' strengths.
Benefits
- Improved predictive accuracy through ensemble learning.
- Enhanced model robustness by combining different algorithms.
- Increased reliability in diabetes detection.
- Contribution to healthcare applications for better patient outcomes.
- Valuable resource for the data science and machine learning community.
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Priority
Medium
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