fixing docs

Author: Christian Jorgensen

src/skmatter/decomposition/_kernel_pcovc.py

@@ -303,7 +303,7 @@ def fit(self, X, Y, W=None):
             not passed, it is assumed that the weights will be taken from a
             linear classifier fit between :math:`\mathbf{X}` and :math:`\mathbf{Y}`.
             In the multioutput case, use
-            `` W = np.hstack([est_.coef_.T for est_ in classifier.estimators_])``.
+            ``W = np.hstack([est_.coef_.T for est_ in classifier.estimators_])``.
 
         Returns
         -------
@@ -514,11 +514,11 @@ def decision_function(self, X=None, T=None):
 
         Returns
         -------
-        Z : numpy.ndarray, shape (n_samples,) or (n_samples, n_classes), or a list of \
-                n_outputs_ such arrays if n_outputs_ > 1
+        Z : numpy.ndarray, shape (n_samples,) or (n_samples, n_classes), or
+            a list of n_outputs such arrays if n_outputs > 1.
             Confidence scores. For binary classification, has shape `(n_samples,)`,
             for multiclass classification, has shape `(n_samples, n_classes)`.
-            If n_outputs_ > 1, the list can contain arrays with differing shapes
+            If n_outputs > 1, the list can contain arrays with differing shapes
             depending on the number of classes in each output of Y.
         """
         check_is_fitted(self, attributes=["pkz_", "ptz_"])

src/skmatter/decomposition/_pcovc.py

@@ -133,8 +133,8 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         `sklearn.pipeline.Pipeline` with model caching.
         In such cases, the classifier will be re-fitted on the same
         training data as the composite estimator.
-        If None and ``n_outputs < 2``, ``sklearn.linear_model.LogisticRegression()`` is used.
-        If None and ``n_outputs >= 2``, a ``sklearn.multioutput.MultiOutputClassifier()`` is
+        If None and ``n_outputs_ < 2``, ``sklearn.linear_model.LogisticRegression()`` is used.
+        If None and ``n_outputs_ >= 2``, a ``sklearn.multioutput.MultiOutputClassifier()`` is
         constructed, with ``sklearn.linear_model.LogisticRegression()`` models used for each
         label.
 
@@ -301,7 +301,7 @@ def fit(self, X, Y, W=None):
             not passed, it is assumed that the weights will be taken from a
             linear classifier fit between :math:`\mathbf{X}` and :math:`\mathbf{Y}`.
             In the multioutput case, use
-            `` W = np.hstack([est_.coef_.T for est_ in classifier.estimators_])``.
+            ``W = np.hstack([est_.coef_.T for est_ in classifier.estimators_])``.
         """
         X, Y = validate_data(self, X, Y, multi_output=True, y_numeric=False)