scikit-learn
diff --git a/‎sklearn/linear_model/_logistic.py
Lines changed: 18 additions & 21 deletions b/‎sklearn/linear_model/_logistic.py
Lines changed: 18 additions & 21 deletions
@@ -624,34 +624,32 @@ def _log_reg_scoring_path(
     test : list of indices
         The indices of the test set.
 
-    pos_class : int, default=None
+    pos_class : int
         The class with respect to which we perform a one-vs-all fit.
         If None, then it is assumed that the given problem is binary.
 
-    Cs : int or list of floats, default=10
+    Cs : int or list of floats
         Each of the values in Cs describes the inverse of
         regularization strength. If Cs is as an int, then a grid of Cs
         values are chosen in a logarithmic scale between 1e-4 and 1e4.
-        If not provided, then a fixed set of values for Cs are used.
 
-    scoring : callable, default=None
+    scoring : callable
         A string (see model evaluation documentation) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``. For a list of scoring functions
-        that can be used, look at :mod:`sklearn.metrics`. The
-        default scoring option used is accuracy_score.
+        that can be used, look at :mod:`sklearn.metrics`.
 
-    fit_intercept : bool, default=False
+    fit_intercept : bool
         If False, then the bias term is set to zero. Else the last
         term of each coef_ gives us the intercept.
 
-    max_iter : int, default=100
+    max_iter : int
         Maximum number of iterations for the solver.
 
-    tol : float, default=1e-4
+    tol : float
         Tolerance for stopping criteria.
 
-    class_weight : dict or 'balanced', default=None
+    class_weight : dict or 'balanced'
         Weights associated with classes in the form ``{class_label: weight}``.
         If not given, all classes are supposed to have weight one.
 
@@ -662,25 +660,24 @@ def _log_reg_scoring_path(
         Note that these weights will be multiplied with sample_weight (passed
         through the fit method) if sample_weight is specified.
 
-    verbose : int, default=0
+    verbose : int
         For the liblinear and lbfgs solvers set verbose to any positive
         number for verbosity.
 
-    solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, \
-            default='lbfgs'
+    solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}
         Decides which solver to use.
 
-    penalty : {'l1', 'l2', 'elasticnet'}, default='l2'
+    penalty : {'l1', 'l2', 'elasticnet'}
         Used to specify the norm used in the penalization. The 'newton-cg',
         'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
         only supported by the 'saga' solver.
 
-    dual : bool, default=False
+    dual : bool
         Dual or primal formulation. Dual formulation is only implemented for
         l2 penalty with liblinear solver. Prefer dual=False when
         n_samples > n_features.
 
-    intercept_scaling : float, default=1.
+    intercept_scaling : float
         Useful only when the solver 'liblinear' is used
         and self.fit_intercept is set to True. In this case, x becomes
         [x, self.intercept_scaling],
@@ -692,26 +689,26 @@ def _log_reg_scoring_path(
         To lessen the effect of regularization on synthetic feature weight
         (and therefore on the intercept) intercept_scaling has to be increased.
 
-    multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'
+    multi_class : {'auto', 'ovr', 'multinomial'}
         If the option chosen is 'ovr', then a binary problem is fit for each
         label. For 'multinomial' the loss minimised is the multinomial loss fit
         across the entire probability distribution, *even when the data is
         binary*. 'multinomial' is unavailable when solver='liblinear'.
 
-    random_state : int, RandomState instance, default=None
+    random_state : int, RandomState instance
         Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
         data. See :term:`Glossary <random_state>` for details.
 
-    max_squared_sum : float, default=None
+    max_squared_sum : float
         Maximum squared sum of X over samples. Used only in SAG solver.
         If None, it will be computed, going through all the samples.
         The value should be precomputed to speed up cross validation.
 
-    sample_weight : array-like of shape(n_samples,), default=None
+    sample_weight : array-like of shape(n_samples,)
         Array of weights that are assigned to individual samples.
         If not provided, then each sample is given unit weight.
 
-    l1_ratio : float, default=None
+    l1_ratio : float
         The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
         used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
         to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent