scikit-learn
diff --git a/‎sklearn/linear_model/ridge.py
Lines changed: 27 additions & 27 deletions b/‎sklearn/linear_model/ridge.py
Lines changed: 27 additions & 27 deletions
@@ -262,7 +262,7 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
         assumed to be specific to the targets. Hence they must correspond in
         number.
 
-    sample_weight : float or numpy array of shape [n_samples], default None
+    sample_weight : float or numpy array of shape [n_samples], default=None
         Individual weights for each sample. If sample_weight is not None and
         solver='auto', the solver will be set to 'cholesky'.
 
@@ -314,10 +314,10 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
         by scipy.sparse.linalg. For 'sag' and saga solver, the default value is
         1000.
 
-    tol : float, default 1e-3
+    tol : float, default=1e-3
         Precision of the solution.
 
-    verbose : int, default 0
+    verbose : int, default=0
         Verbosity level. Setting verbose > 0 will display additional
         information depending on the solver used.
 
@@ -328,21 +328,21 @@ def ridge_regression(X, y, alpha, sample_weight=None, solver='auto',
         generator; If None, the random number generator is the RandomState
         instance used by `np.random`. Used when ``solver`` == 'sag'.
 
-    return_n_iter : boolean, default False
+    return_n_iter : boolean, default=False
         If True, the method also returns `n_iter`, the actual number of
         iteration performed by the solver.
 
         .. versionadded:: 0.17
 
-    return_intercept : boolean, default False
+    return_intercept : boolean, default=False
         If True and if X is sparse, the method also returns the intercept,
         and the solver is automatically changed to 'sag'. This is only a
         temporary fix for fitting the intercept with sparse data. For dense
         data, use sklearn.linear_model._preprocess_data before your regression.
 
         .. versionadded:: 0.17
 
-    check_input : boolean, default True
+    check_input : boolean, default=True
         If False, the input arrays X and y will not be checked.
 
         .. versionadded:: 0.21
@@ -619,7 +619,7 @@ class Ridge(_BaseRidge, RegressorMixin):
 
     Parameters
     ----------
-    alpha : {float, array-like}, shape (n_targets), default 1.0
+    alpha : {float, array-like}, shape (n_targets), default=1.0
         Regularization strength; must be a positive float. Regularization
         improves the conditioning of the problem and reduces the variance of
         the estimates. Larger values specify stronger regularization.
@@ -628,28 +628,28 @@ class Ridge(_BaseRidge, RegressorMixin):
         assumed to be specific to the targets. Hence they must correspond in
         number.
 
-    fit_intercept : bool, default True
+    fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
         (e.g. data is expected to be already centered).
 
-    normalize : boolean, optional, default False
+    normalize : boolean, optional, default=False
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True, the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
         If you wish to standardize, please use
         :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
-    copy_X : boolean, optional, default True
+    copy_X : boolean, optional, default=True
         If True, X will be copied; else, it may be overwritten.
 
     max_iter : int, optional
         Maximum number of iterations for conjugate gradient solver.
         For 'sparse_cg' and 'lsqr' solvers, the default value is determined
         by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.
 
-    tol : float, default 1e-3
+    tol : float, default=1e-3
         Precision of the solution.
 
     solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'}
@@ -771,34 +771,34 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
 
     Parameters
     ----------
-    alpha : float, default 1.0
+    alpha : float, default=1.0
         Regularization strength; must be a positive float. Regularization
         improves the conditioning of the problem and reduces the variance of
         the estimates. Larger values specify stronger regularization.
         Alpha corresponds to ``C^-1`` in other linear models such as
         LogisticRegression or LinearSVC.
 
-    fit_intercept : bool, default True
+    fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set to false, no
         intercept will be used in calculations (e.g. data is expected to be
         already centered).
 
-    normalize : boolean, optional, default False
+    normalize : boolean, optional, default=False
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True, the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
         If you wish to standardize, please use
         :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
-    copy_X : boolean, optional, default True
+    copy_X : boolean, optional, default=True
         If True, X will be copied; else, it may be overwritten.
 
     max_iter : int, optional
         Maximum number of iterations for conjugate gradient solver.
         The default value is determined by scipy.sparse.linalg.
-    tol : float, default 1e-3
+    tol : float, default=1e-3
         Precision of the solution.
 
     class_weight : dict or 'balanced', optional
@@ -843,7 +843,7 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
           .. versionadded:: 0.19
            SAGA solver.
 
-    random_state : int, RandomState instance or None, optional, default None
+    random_state : int, RandomState instance or None, optional, default=None
         The seed of the pseudo random number generator to use when shuffling
         the data.  If int, random_state is the seed used by the random number
         generator; If RandomState instance, random_state is the random number
@@ -906,7 +906,7 @@ def fit(self, X, y, sample_weight=None):
         y : array-like, shape = [n_samples]
             Target values
 
-        sample_weight : {float, numpy array}, shape (n_samples), default None
+        sample_weight : {float, numpy array}, shape (n_samples), default=None
             Sample weight.
 
             .. versionadded:: 0.17
@@ -1586,7 +1586,7 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
 
     Parameters
     ----------
-    alphas : numpy array of shape [n_alphas], default (0.1, 1.0, 10.0)
+    alphas : numpy array of shape [n_alphas], default=(0.1, 1.0, 10.0)
         Array of alpha values to try.
         Regularization strength; must be a positive float. Regularization
         improves the conditioning of the problem and reduces the variance of
@@ -1595,20 +1595,20 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
         LogisticRegression or LinearSVC.
         If using generalized cross-validation, alphas must be positive.
 
-    fit_intercept : bool, default True
+    fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
         (e.g. data is expected to be already centered).
 
-    normalize : boolean, optional, default False
+    normalize : boolean, optional, default=False
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True, the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
         If you wish to standardize, please use
         :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
-    scoring : string, callable or None, optional, default: None
+    scoring : string, callable or None, optional, default=None
         A string (see model evaluation documentation) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``.
@@ -1700,28 +1700,28 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
 
     Parameters
     ----------
-    alphas : numpy array of shape [n_alphas], default (0.1, 1.0, 10.0)
+    alphas : numpy array of shape [n_alphas], default=(0.1, 1.0, 10.0)
         Array of alpha values to try.
         Regularization strength; must be a positive float. Regularization
         improves the conditioning of the problem and reduces the variance of
         the estimates. Larger values specify stronger regularization.
         Alpha corresponds to ``C^-1`` in other linear models such as
         LogisticRegression or LinearSVC.
 
-    fit_intercept : bool, default True
+    fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
         to false, no intercept will be used in calculations
         (e.g. data is expected to be already centered).
 
-    normalize : boolean, optional, default False
+    normalize : boolean, optional, default=False
         This parameter is ignored when ``fit_intercept`` is set to False.
         If True, the regressors X will be normalized before regression by
         subtracting the mean and dividing by the l2-norm.
         If you wish to standardize, please use
         :class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
         on an estimator with ``normalize=False``.
 
-    scoring : string, callable or None, optional, default: None
+    scoring : string, callable or None, optional, default=None
         A string (see model evaluation documentation) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``.
@@ -1815,7 +1815,7 @@ def fit(self, X, y, sample_weight=None):
         y : array-like, shape (n_samples,)
             Target values. Will be cast to X's dtype if necessary
 
-        sample_weight : {float, numpy array}, shape (n_samples), default None
+        sample_weight : {float, numpy array}, shape (n_samples), default=None
             Sample weight.
 
         Returns