Using numpy, matplotlib, sklearn libraries, how can you predict whether a person has Parkinson’s disease or not?
- Feature Selection:
- Explain the process of feature selection for Parkinson's disease detection. What features are commonly considered, and how do they contribute to the model's accuracy?
- Data Preprocessing:
- Describe the essential steps in preprocessing the dataset for a Parkinson's disease detection model. How do you handle missing data, outliers, and ensure data quality?
- Algorithm Selection:
- Compare the performance of different machine learning algorithms for Parkinson's disease detection. What factors influence your choice of algorithms, and how do they handle the specific characteristics of medical datasets?
- Cross-Validation Techniques:
- Discuss the importance of cross-validation in evaluating the performance of your model. Explain the use of k-fold cross-validation and how it mitigates overfitting in the context of Parkinson's disease detection.
- Imbalanced Datasets:
- Parkinson's disease datasets often exhibit class imbalance. How would you address this issue during the training phase, and what metrics would you use to evaluate model performance?