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Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.

Now, which of the following option will be correct?

A. It is more likely for X1 to be excluded from the model

B. It is more likely for X1 to be included in the model

C. Can’t say

D. None of these

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Solution: B

Big feature values = smaller coefficients = less lasso penalty = more likely to have be kept

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