A machine learning model is suffering from high computational costs and overfitting. How could dimensionality reduction be implemented to solve these problems?
- Add more features
- Apply PCA or LDA, depending on the data type
- Increase the model's complexity
- Reduce the dataset size
Applying dimensionality reduction techniques like PCA or LDA can significantly reduce the feature space and computational costs without losing important information. This can also help in addressing overfitting by simplifying the model, making it less likely to capture noise in the data. Increasing model complexity or adding more features would exacerbate the problem, and reducing the dataset size may lead to loss of information.
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