Changed single ticks to double (#2640)

Co-authored-by: Matthew Middlehurst <[email protected]>

Author: Val-2608

aeon/regression/_dummy.py

@@ -15,13 +15,13 @@ class DummyRegressor(BaseRegressor):
 
     This regressor is a wrapper for the scikit-learn DummyClassifier that serves as a
     simple baseline to compare against other more complex regressors.
-    The specific behaviour of the baseline is selected with the `strategy` parameter.
+    The specific behaviour of the baseline is selected with the ``strategy`` parameter.
 
     All strategies make predictions that ignore the input feature values passed
-    as the `X` argument to `fit` and `predict`. The predictions, however,
-    typically depend on values observed in the `y` parameter passed to `fit`.
+    as the ``X`` argument to ``fit`` and ``predict``. The predictions, however,
+    typically depend on values observed in the ``y`` parameter passed to ``fit``.
 
-    Function-identical to `sklearn.dummy.DummyRegressor`, which is called inside.
+    Function-identical to ``sklearn.dummy.DummyRegressor``, which is called inside.
 
     Parameters
     ----------

aeon/regression/base.py

@@ -167,7 +167,7 @@ def fit_predict(self, X, y) -> np.ndarray:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -208,7 +208,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -220,7 +220,7 @@ def score(self, X, y, metric="r2", metric_params=None) -> float:
             (ground truth) for fitting indices corresponding to instance indices in X.
         metric : Union[str, callable], default="r2",
             Defines the scoring metric to test the fit of the model. For supported
-            strings arguments, check `sklearn.metrics.get_scorer_names`.
+            strings arguments, check ``sklearn.metrics.get_scorer_names``.
         metric_params : dict, default=None,
             Contains parameters to be passed to the scoring function. If None, no
             parameters are passed.