How does multicollinearity affect feature selection?

It affects the accuracy of the model
It causes unstable parameter estimates
It makes the model less interpretable
It results in high variance of the model

Multicollinearity, which refers to the high correlation between predictor variables, can affect feature selection by causing unstable estimates of the parameters. This instability can lead to strange and unreliable predictions, making the feature selection process less accurate.

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