diff --git a/docs/source/developer_guide/dependencies.rst b/docs/source/developer_guide/dependencies.rst
index 86e9a924060..f70455b8e66 100644
--- a/docs/source/developer_guide/dependencies.rst
+++ b/docs/source/developer_guide/dependencies.rst
@@ -42,7 +42,7 @@ Estimators with a soft dependency need to ensure the following:
    or a ``list`` of ``str``, of import dependencies. Exceptions will automatically raised when constructing the estimator
    in an environment without the required packages.
 *  In a case where the package import differs from the package name, i.e., ``import package_string`` is different from
-   ``pip install different-package-string`` (usually the case for packages containing a dash in the name), the ``python_dependecies_alias`` tag
+   ``pip install different-package-string`` (usually the case for packages containing a dash in the name), the ``python_dependencies_alias`` tag
    should be populated to pass the information on package and import strings as ``dict`` such as ``{"scikit-learn":"sklearn"}``.
 *  If the soft dependencies require specific python versions, the ``python_version``
    tag should also be populated, with a PEP 440 compliant version specification ``str`` such as ``"<3.10"`` or ``">3.6,~=3.8"``.
diff --git a/sktime/alignment/dtw_python.py b/sktime/alignment/dtw_python.py
index a43d07ea605..7bddbf580d2 100644
--- a/sktime/alignment/dtw_python.py
+++ b/sktime/alignment/dtw_python.py
@@ -51,7 +51,7 @@ class AlignerDTW(BaseAligner):
         "capability:distance": True,  # does compute/return overall distance?
         "capability:distance-matrix": True,  # does compute/return distance matrix?
         "python_dependencies": "dtw-python",
-        "python_dependecies_alias": {"dtw-python": "dtw"},
+        "python_dependencies_alias": {"dtw-python": "dtw"},
     }
 
     def __init__(
diff --git a/sktime/base/_base.py b/sktime/base/_base.py
index 22ebea82559..0546071fcd1 100644
--- a/sktime/base/_base.py
+++ b/sktime/base/_base.py
@@ -357,6 +357,9 @@ class BaseEstimator(BaseObject):
     Extends sktime's BaseObject to include basic functionality for fittable estimators.
     """
 
+    # global dependency alias tag for sklearn dependency management
+    _tags = {"python_dependencies_alias": {"scikit-learn": "sklearn"}}
+
     def __init__(self):
         self._is_fitted = False
         super().__init__()
diff --git a/sktime/classification/distance_based/_elastic_ensemble.py b/sktime/classification/distance_based/_elastic_ensemble.py
index 9c553c41511..4ff8eed4791 100644
--- a/sktime/classification/distance_based/_elastic_ensemble.py
+++ b/sktime/classification/distance_based/_elastic_ensemble.py
@@ -290,7 +290,7 @@ def _fit(self, X, y):
                     param_distributions=ElasticEnsemble._get_100_param_options(
                         self.distance_measures[dm], X
                     ),
-                    n_iter=100 * self.proportion_of_param_options,
+                    n_iter=int(100 * self.proportion_of_param_options),
                     cv=LeaveOneOut(),
                     scoring="accuracy",
                     n_jobs=self._threads_to_use,
diff --git a/sktime/classification/feature_based/_matrix_profile_classifier.py b/sktime/classification/feature_based/_matrix_profile_classifier.py
index bb32e24802b..448720cd470 100644
--- a/sktime/classification/feature_based/_matrix_profile_classifier.py
+++ b/sktime/classification/feature_based/_matrix_profile_classifier.py
@@ -57,17 +57,20 @@ class MatrixProfileClassifier(BaseClassifier):
     >>> from sktime.classification.feature_based import MatrixProfileClassifier
     >>> from sktime.datasets import load_unit_test
     >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
-    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
-    >>> clf = MatrixProfileClassifier()
-    >>> clf.fit(X_train, y_train)
-    MatrixProfileClassifier(...)
-    >>> y_pred = clf.predict(X_test)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)  # doctest: +SKIP
+    >>> clf = MatrixProfileClassifier()  # doctest: +SKIP
+    >>> clf.fit(X_train, y_train)  # doctest: +SKIP
+    MatrixProfileClassifier(...)  # doctest: +SKIP
+    >>> y_pred = clf.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {
         "capability:multithreading": True,
         "capability:predict_proba": True,
         "classifier_type": "distance",
+        # sklearn 1.3.0 has a bug which causes predict_proba to fail
+        # see scikit-learn#26768 and sktime#4778
+        "python_dependencies": "scikit-learn!=1.3.0",
     }
 
     def __init__(