add dual_gap_ attribute
@@ -977,6 +977,10 @@ class Lasso(ElasticNet):
coef_ : ndarray of shape (n_features,) or (n_targets, n_features)
parameter vector (w in the cost function formula)
+ dual_gap_ : float or ndarray of shape (n_targets,)
+ Given param alpha, the dual gaps at the end of the optimization,
+ same shape as each observation of y.
+
sparse_coef_ : sparse matrix of shape (n_features, 1) or \
(n_targets, n_features)
``sparse_coef_`` is a readonly property derived from ``coef_``
