How about ElasticNet?

Should it default to median for ENet?

I meant that even Non-transformers, can have transform methods.

IMO, that's a contradiction, effectively. By implementing fit() and
transform(), they implement the Transformer API, and are thus transformers.
The common tests should be explicitly non-mutually-exclusive between
predictors and transformers. What sorts of tests were failing?

Having said that, I'm not sure I agree with this design and might rather
see this style of feature selection come through a wrapping meta-estimator
than a mixin.

On 12 February 2015 at 17:40, Manoj Kumar notifications@github.com wrote:

In sklearn/feature_selection/from_model.py
#4241 (comment)
:

@@ -68,11 +71,16 @@ def transform(self, X, threshold=None):

     # Retrieve threshold
     if threshold is None:

•        if hasattr(self, "penalty") and self.penalty == "l1":
  

•        threshold = getattr(self, "threshold", None)
  

•    if threshold is None:
  

•        # Lasso has a l1 penalty but no penalty param.
  

•        if (hasattr(self, "penalty") and self.penalty == "l1" or
  

•            'Lasso' in self.**class**.**name**):
  

Should it default to median for ENet?

—
Reply to this email directly or view it on GitHub
https://github.com/scikit-learn/scikit-learn/pull/4241/files#r24561488.

Hmm. Yes that seems a better idea.

However, sorry if this question seems dumb, but is it worth writing a separate meta-estimator, to just do a masking of X corresponding to coefs, below a certain threshold value?

Oh, I see work has already been done in deprecating _LearntSelectorMixin and having a SelectfromModel estimator. How far is it from being merged?

I didn't mean to comment on this line of code. @maheshakya tried implementing a meta-estimator version at #3011. But there are arguments in both directions.