Hi everyone,
I am using the sklearn.linear_model.ElasticNet class to fit some data. The
structure of the data is y = Xw, and I am trying to solve for w where
y.shape is (150,) and X.shape is (150,150), with a non-negativity
constraint. Both y and each column of X is mean-removed. Some of the
columns of X are quite correlated with each other. I have been playing
around a bit with different settings of inputs to the initialization of
ElasticNet and I am running into the following issue understanding alpha
and rho: for a given value of alpha (rather small, alpha=0.0075) , when I
change rho from 0 to 0.5 to 1, I get smaller L1 norm (np.sum(w)) and a
larger L2 norm (np.sum(w**2)). This defies my intuition that larger values
of rho should make ElasticNet more and more averse to growing L2 norm and
less and less averse to growing L1 norm, so I was expecting the exact
opposite. What is the explanation for this behavior?
Thanks!
Ariel
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