LogisticregressionCV docs(scikit-learn#8424): documented C_ shape

sklearn/linear_model/logistic.py

@@ -213,7 +213,7 @@ def _logistic_grad_hess(w, X, y, alpha, sample_weight=None):
     d = sample_weight * z * (1 - z)
     if sparse.issparse(X):
         dX = safe_sparse_dot(sparse.dia_matrix((d, 0),
-                             shape=(n_samples, n_samples)), X)
+                            shape=(n_samples, n_samples)), X)
     else:
         # Precompute as much as possible
         dX = d[:, np.newaxis] * X
@@ -1488,6 +1488,7 @@ class LogisticRegressionCV(LogisticRegression, BaseEstimator,
         Array of C that maps to the best scores across every class. If refit is
         set to False, then for each class, the best C is the average of the
         C's that correspond to the best scores for each fold.
+        `C_` is of shape(n_classes,) when the problem is binary.
 
     n_iter_ : array, shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs)
         Actual number of iterations for all classes, folds and Cs.