Also, assuming:
X.shape is (30,54)
y.shape is (30,)
When I run
from sklearn.linear_model import RidgeCV
reg = RidgeCV(store_cv_values=True)
reg.fit(X,y)
I get
> reg.cv_values_.shape
(30,3)
>From what I read in the documentation, by default RidgeCV uses (efficient
Leave-One-Out), so I would imagine I would get 30 scores (one per sample),
but why do I have three columns? What are these numbers supposed to
represent?
Thanks,
Josh
On Wed, Oct 23, 2013 at 3:38 PM, Josh Wasserstein <[email protected]>wrote:
> According to the documentation of RidgeCV, the default scoring is *None. *What
> does that mean? Which scoring metric does it use?
>
> Shouldn't it be one of:
> * Explained variance
> * Mean absolute error
> * Mean squared error
> * R^2
>
> Here is the entry from the documentation:
>
> *scoring* : string, callable or None, optional, default: None
>
> A string (see model evaluation documentation) or a scorer callable object
> / function with signature scorer(estimator, X, y).
>
> Thanks,
>
> Josh
>
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