Imagine you are using Lasso Regression in a highly multicollinear dataset. What effect might this choice of model have and why?
- It might ignore all correlated variables.
- It might lead to high bias.
- It might lead to overfitting.
- It might randomly select one variable from a group of correlated variables.
Lasso regression is a regularization method that can shrink some coefficients to zero, effectively performing feature selection. In the presence of highly correlated variables, Lasso tends to randomly select one from a group of correlated variables, leaving the others being shrunk to zero.
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