You're using a model that is sensitive to multicollinearity. How can feature selection help improve your model's performance?
- By adding more features
- By removing highly correlated features
- By transforming the features
- By using all features
If you're using a model that is sensitive to multicollinearity, feature selection can help improve the model's performance by removing highly correlated features. Multicollinearity can affect the stability and performance of some models, and removing features that are highly correlated with others can alleviate this problem.
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