If you are facing multicollinearity in your regression model, how can dimensionality reduction techniques be employed to mitigate this issue?
- Increase the number of observations
- Apply PCA and use principal components
- Add interaction terms
- Use a non-linear regression model
Multicollinearity arises when features are highly correlated with each other, and it can be mitigated by applying PCA. By transforming the data into principal components, which are uncorrelated, the multicollinearity problem is resolved. Using the principal components in the regression model ensures that the feature relationships are captured without redundancy. Other options do not address the issue of multicollinearity directly.
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