DOC: Fix

thomasjpfan · thomasjpfan · commit e92ba53e5458 · 2018-06-17T09:34:39.000-04:00
diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
@@ -216,7 +216,7 @@ 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
@@ -1800,7 +1800,7 @@ def score(self, X, y, sample_weight=None):
         X : array-like, shape = (n_samples, n_features)
             Test samples.
 
-        y : array-like, shape = (n_samples) or (n_samples, n_outputs)
+        y : array-like, shape = (n_samples,)
             True labels for X.
 
         sample_weight : array-like, shape = [n_samples], optional
