DOC backticks in attribute docstrings unnecessaru since scikit-learn#3489

Author: jnothman

sklearn/calibration.py

@@ -428,10 +428,10 @@ class _SigmoidCalibration(BaseEstimator, RegressorMixin):
 
     Attributes
     ----------
-    `a_` : float
+    a_ : float
         The slope.
 
-    `b_` : float
+    b_ : float
         The intercept.
     """
     def fit(self, X, y, sample_weight=None):
@@ -470,7 +470,7 @@ def predict(self, T):
 
         Returns
         -------
-        `T_` : array, shape (n_samples,)
+        T_ : array, shape (n_samples,)
             The predicted data.
         """
         T = column_or_1d(T)

sklearn/dummy.py

@@ -66,7 +66,7 @@ class DummyClassifier(BaseEstimator, ClassifierMixin):
     outputs_2d_ : bool,
         True if the output at fit is 2d, else false.
 
-    `sparse_output_` : bool,
+    sparse_output_ : bool,
         True if the array returned from predict is to be in sparse CSC format.
         Is automatically set to True if the input y is passed in sparse format.
 

sklearn/linear_model/stochastic_gradient.py

@@ -1203,10 +1203,10 @@ class SGDRegressor(BaseSGDRegressor, _LearntSelectorMixin):
     intercept_ : array, shape (1,)
         The intercept term.
 
-    `average_coef_` : array, shape (n_features,)
+    average_coef_ : array, shape (n_features,)
         Averaged weights assigned to the features.
 
-    `average_intercept_` : array, shape (1,)
+    average_intercept_ : array, shape (1,)
         The averaged intercept term.
 
     Examples

sklearn/linear_model/theil_sen.py

@@ -255,16 +255,16 @@ class TheilSenRegressor(LinearModel, RegressorMixin):
 
     Attributes
     ----------
-    `coef_` : array, shape = (n_features)
+    coef_ : array, shape = (n_features)
         Coefficients of the regression model (median of distribution).
 
-    `intercept_` : float
+    intercept_ : float
         Estimated intercept of regression model.
 
-    `breakdown_` : float
+    breakdown_ : float
         Approximated breakdown point.
 
-    `n_iter_` : int
+    n_iter_ : int
         Number of iterations needed for the spatial median.
 
     n_subpopulation_ : int

sklearn/preprocessing/data.py

@@ -478,10 +478,10 @@ class RobustScaler(BaseEstimator, TransformerMixin):
 
     Attributes
     ----------
-    `center_` : array of floats
+    center_ : array of floats
         The median value for each feature in the training set.
 
-    `scale_` : array of floats
+    scale_ : array of floats
         The (scaled) interquartile range for each feature in the training set.
 
     See also