Repository: aeon-toolkit/aeon
Pull request #2237 (2024-10-21 18:01:29)

[REF,ENH] Refactor test params/instance methods and disable `sklearn` metadata function creation
- renames `get_test_params` to make it protected
- renames `create_test_instance` to make it protected
- updates docs where I found cases of the above. Removes part of the `_get_test_params` docstring because it isnt really true or relevant.
- overloads `get_metadata_routing` and  `_get_default_requests` from `BaseEstimator` to raise errors. I'm not familiar with this framework, it is disabled by default in `sklearn` and I dont believe it is compatible with any of our estimators. It currently generates a bunch of functions, this should stop that.

aeon-actions-bot[bot]: 
## Thank you for contributing to `aeon`

I have added the following labels to this PR based on the title: **[ $\\color{#FEF1BE}{\\textsf{enhancement}}$, $\\color{#BDD9DB}{\\textsf{refactor}}$ ]**.
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Patch:
diff --git a/aeon/anomaly_detection/_dwt_mlead.py b/aeon/anomaly_detection/_dwt_mlead.py
index 73772ace..2d9a036b 100644
--- a/aeon/anomaly_detection/_dwt_mlead.py
+++ b/aeon/anomaly_detection/_dwt_mlead.py
@@ -236,7 +236,7 @@ class DWT_MLEAD(BaseAnomalyDetector):
         return counter[:n]
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Only supports 'default'-parameter set.
@@ -253,7 +253,6 @@ class DWT_MLEAD(BaseAnomalyDetector):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "start_level": 2,
diff --git a/aeon/anomaly_detection/_iforest.py b/aeon/anomaly_detection/_iforest.py
index 54469c02..c0980309 100644
--- a/aeon/anomaly_detection/_iforest.py
+++ b/aeon/anomaly_detection/_iforest.py
@@ -137,7 +137,7 @@ class IsolationForest(PyODAdapter):
         return super()._fit_predict(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -152,7 +152,6 @@ class IsolationForest(PyODAdapter):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `IsolationForest(**params)` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "n_estimators": 10,
diff --git a/aeon/anomaly_detection/_kmeans.py b/aeon/anomaly_detection/_kmeans.py
index 403aee5f..07b98ebe 100644
--- a/aeon/anomaly_detection/_kmeans.py
+++ b/aeon/anomaly_detection/_kmeans.py
@@ -171,7 +171,7 @@ class KMeansAD(BaseAnomalyDetector):
         return point_anomaly_scores
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -186,7 +186,6 @@ class KMeansAD(BaseAnomalyDetector):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "n_clusters": 5,
diff --git a/aeon/anomaly_detection/_merlin.py b/aeon/anomaly_detection/_merlin.py
index e86020d0..36474553 100644
--- a/aeon/anomaly_detection/_merlin.py
+++ b/aeon/anomaly_detection/_merlin.py
@@ -208,7 +208,7 @@ class MERLIN(BaseAnomalyDetector):
         return C[d_max], np.sqrt(D[d_max])
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -223,6 +223,5 @@ class MERLIN(BaseAnomalyDetector):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {"min_length": 4, "max_length": 7}
diff --git a/aeon/anomaly_detection/_pyodadapter.py b/aeon/anomaly_detection/_pyodadapter.py
index e492d718..637e9340 100644
--- a/aeon/anomaly_detection/_pyodadapter.py
+++ b/aeon/anomaly_detection/_pyodadapter.py
@@ -158,7 +158,7 @@ class PyODAdapter(BaseAnomalyDetector):
         return point_anomaly_scores
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -173,7 +173,6 @@ class PyODAdapter(BaseAnomalyDetector):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         _check_soft_dependencies(*cls._tags["python_dependencies"])
 
diff --git a/aeon/anomaly_detection/_stomp.py b/aeon/anomaly_detection/_stomp.py
index d6f988a5..93d89c44 100644
--- a/aeon/anomaly_detection/_stomp.py
+++ b/aeon/anomaly_detection/_stomp.py
@@ -118,7 +118,7 @@ class STOMP(BaseAnomalyDetector):
             )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -133,7 +133,6 @@ class STOMP(BaseAnomalyDetector):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "window_size": 10,
diff --git a/aeon/base/_base.py b/aeon/base/_base.py
index 402cd9ea..5fef2c05 100644
--- a/aeon/base/_base.py
+++ b/aeon/base/_base.py
@@ -20,13 +20,14 @@ class BaseAeonEstimator(BaseEstimator, ABC):
     Contains the following methods:
 
     reset estimator to post-init  - reset(keep)
-    clonee stimator (copy)        - clone(random_state)
+    clone stimator (copy)         - clone(random_state)
     inspect tags (class method)   - get_class_tags()
     inspect tags (one tag, class) - get_class_tag(tag_name, tag_value_default,
                                                                     raise_error)
     inspect tags (all)            - get_tags()
     inspect tags (one tag)        - get_tag(tag_name, tag_value_default, raise_error)
     setting dynamic tags          - set_tags(**tag_dict)
+    get fitted parameters         - get_fitted_params(deep)
 
     All estimators have the attribute:
 
@@ -349,7 +350,7 @@ class BaseAeonEstimator(BaseEstimator, ABC):
             )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """
         Return testing parameter settings for the estimator.
 
@@ -365,17 +366,16 @@ class BaseAeonEstimator(BaseEstimator, ABC):
             Parameters to create testing instances of the class. Each dict are
             parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         # default parameters = empty dict
         return {}
 
     @classmethod
-    def create_test_instance(cls, parameter_set="default", return_first=True):
+    def _create_test_instance(cls, parameter_set="default", return_first=True):
         """
         Construct Estimator instance if possible.
 
-        Calls the `get_test_params` method and returns an instance or list of instances
+        Calls the `_get_test_params` method and returns an instance or list of instances
         using the returned dict or list of dict.
 
         Parameters
@@ -393,7 +393,7 @@ class BaseAeonEstimator(BaseEstimator, ABC):
             Instance of the class with default parameters. If return_first
             is False, returns list of instances.
         """
-        params = cls.get_test_params(parameter_set=parameter_set)
+        params = cls._get_test_params(parameter_set=parameter_set)
 
         if isinstance(params, list):
             if return_first:
@@ -418,6 +418,19 @@ class BaseAeonEstimator(BaseEstimator, ABC):
             "aeon estimators do not have a _validate_data method."
         )
 
+    def get_metadata_routing(self):
+        """Sklearn metadata routing."""
+        raise NotImplementedError(
+            "aeon estimators do not have a get_metadata_routing method."
+        )
+
+    @classmethod
+    def _get_default_requests(cls):
+        """Sklearn metadata request defaults."""
+        from sklearn.utils._metadata_requests import MetadataRequest
+
+        return MetadataRequest(None)
+
 
 def _clone_estimator(base_estimator, random_state=None):
     """Clone an estimator."""
diff --git a/aeon/classification/compose/_channel_ensemble.py b/aeon/classification/compose/_channel_ensemble.py
index 1a967a75..e8a2f340 100644
--- a/aeon/classification/compose/_channel_ensemble.py
+++ b/aeon/classification/compose/_channel_ensemble.py
@@ -248,7 +248,7 @@ class ChannelEnsembleClassifier(_BaseChannelEnsembleClassifier):
         super().__init__(estimators, verbose=verbose)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -267,7 +267,6 @@ class ChannelEnsembleClassifier(_BaseChannelEnsembleClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.classification.dictionary_based import ContractableBOSS
         from aeon.classification.interval_based import CanonicalIntervalForestClassifier
diff --git a/aeon/classification/compose/_ensemble.py b/aeon/classification/compose/_ensemble.py
index a8df5253..119f016d 100644
--- a/aeon/classification/compose/_ensemble.py
+++ b/aeon/classification/compose/_ensemble.py
@@ -160,7 +160,7 @@ class ClassifierEnsemble(BaseCollectionEnsemble, BaseClassifier):
             return clf
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -175,12 +175,11 @@ class ClassifierEnsemble(BaseCollectionEnsemble, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "classifiers": [
-                KNeighborsTimeSeriesClassifier.create_test_instance(),
-                DummyClassifier.create_test_instance(),
+                KNeighborsTimeSeriesClassifier._create_test_instance(),
+                DummyClassifier._create_test_instance(),
             ],
             "weights": [2, 1],
         }
@@ -408,7 +407,7 @@ class WeightedEnsembleClassifier(_HeterogenousMetaEstimator, BaseClassifier):
         return y_proba
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -423,23 +422,22 @@ class WeightedEnsembleClassifier(_HeterogenousMetaEstimator, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.classification import DummyClassifier
         from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
 
         params1 = {
             "classifiers": [
-                KNeighborsTimeSeriesClassifier.create_test_instance(),
-                DummyClassifier.create_test_instance(),
+                KNeighborsTimeSeriesClassifier._create_test_instance(),
+                DummyClassifier._create_test_instance(),
             ],
             "weights": [42, 1],
         }
 
         params2 = {
             "classifiers": [
-                KNeighborsTimeSeriesClassifier.create_test_instance(),
-                DummyClassifier.create_test_instance(),
+                KNeighborsTimeSeriesClassifier._create_test_instance(),
+                DummyClassifier._create_test_instance(),
             ],
             "weights": 2,
             "cv": 3,
diff --git a/aeon/classification/compose/_pipeline.py b/aeon/classification/compose/_pipeline.py
index 9b2ec98c..69b122fc 100644
--- a/aeon/classification/compose/_pipeline.py
+++ b/aeon/classification/compose/_pipeline.py
@@ -87,7 +87,7 @@ class ClassifierPipeline(BaseCollectionPipeline, BaseClassifier):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -102,7 +102,6 @@ class ClassifierPipeline(BaseCollectionPipeline, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
         from aeon.transformations.collection import Truncator
diff --git a/aeon/classification/convolution_based/_arsenal.py b/aeon/classification/convolution_based/_arsenal.py
index f55afc52..a24a57d9 100644
--- a/aeon/classification/convolution_based/_arsenal.py
+++ b/aeon/classification/convolution_based/_arsenal.py
@@ -395,7 +395,7 @@ class Arsenal(BaseClassifier):
         return results, weight, oob
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -417,7 +417,6 @@ class Arsenal(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"num_kernels": 20, "n_estimators": 5}
diff --git a/aeon/classification/convolution_based/_minirocket.py b/aeon/classification/convolution_based/_minirocket.py
index bb20bb39..dc3ef18a 100644
--- a/aeon/classification/convolution_based/_minirocket.py
+++ b/aeon/classification/convolution_based/_minirocket.py
@@ -194,7 +194,7 @@ class MiniRocketClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -213,7 +213,6 @@ class MiniRocketClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"num_kernels": 100}
diff --git a/aeon/classification/convolution_based/_multirocket.py b/aeon/classification/convolution_based/_multirocket.py
index 7ca894b9..a0c1767e 100644
--- a/aeon/classification/convolution_based/_multirocket.py
+++ b/aeon/classification/convolution_based/_multirocket.py
@@ -198,7 +198,7 @@ class MultiRocketClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -217,7 +217,6 @@ class MultiRocketClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"num_kernels": 100}
diff --git a/aeon/classification/convolution_based/_rocket.py b/aeon/classification/convolution_based/_rocket.py
index c8f82864..8509fde2 100644
--- a/aeon/classification/convolution_based/_rocket.py
+++ b/aeon/classification/convolution_based/_rocket.py
@@ -194,7 +194,7 @@ class RocketClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -213,7 +213,6 @@ class RocketClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"num_kernels": 100}
diff --git a/aeon/classification/deep_learning/_cnn.py b/aeon/classification/deep_learning/_cnn.py
index 771e58d0..5b87cab9 100644
--- a/aeon/classification/deep_learning/_cnn.py
+++ b/aeon/classification/deep_learning/_cnn.py
@@ -302,7 +302,7 @@ class TimeCNNClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -321,7 +321,6 @@ class TimeCNNClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_encoder.py b/aeon/classification/deep_learning/_encoder.py
index 2765c4cb..1773d1ea 100644
--- a/aeon/classification/deep_learning/_encoder.py
+++ b/aeon/classification/deep_learning/_encoder.py
@@ -285,7 +285,7 @@ class EncoderClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -304,7 +304,6 @@ class EncoderClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 8,
diff --git a/aeon/classification/deep_learning/_fcn.py b/aeon/classification/deep_learning/_fcn.py
index 5484f45b..e79ea652 100644
--- a/aeon/classification/deep_learning/_fcn.py
+++ b/aeon/classification/deep_learning/_fcn.py
@@ -306,7 +306,7 @@ class FCNClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -325,7 +325,6 @@ class FCNClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_inception_time.py b/aeon/classification/deep_learning/_inception_time.py
index 135ced4a..8afc1dde 100644
--- a/aeon/classification/deep_learning/_inception_time.py
+++ b/aeon/classification/deep_learning/_inception_time.py
@@ -342,7 +342,7 @@ class InceptionTimeClassifier(BaseClassifier):
         return probs
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -361,7 +361,6 @@ class InceptionTimeClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_classifiers": 1,
@@ -725,7 +724,7 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -744,7 +743,6 @@ class IndividualInceptionClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_lite_time.py b/aeon/classification/deep_learning/_lite_time.py
index 43f58dfd..94e26ced 100644
--- a/aeon/classification/deep_learning/_lite_time.py
+++ b/aeon/classification/deep_learning/_lite_time.py
@@ -258,7 +258,7 @@ class LITETimeClassifier(BaseClassifier):
         return probs
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -277,7 +277,6 @@ class LITETimeClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_classifiers": 1,
@@ -568,7 +567,7 @@ class IndividualLITEClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -587,7 +586,6 @@ class IndividualLITEClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_mlp.py b/aeon/classification/deep_learning/_mlp.py
index 48eb8f71..0f0d538c 100644
--- a/aeon/classification/deep_learning/_mlp.py
+++ b/aeon/classification/deep_learning/_mlp.py
@@ -271,7 +271,7 @@ class MLPClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -290,7 +290,6 @@ class MLPClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_resnet.py b/aeon/classification/deep_learning/_resnet.py
index 963faec2..fe754548 100644
--- a/aeon/classification/deep_learning/_resnet.py
+++ b/aeon/classification/deep_learning/_resnet.py
@@ -317,7 +317,7 @@ class ResNetClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -336,7 +336,6 @@ class ResNetClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 10,
diff --git a/aeon/classification/deep_learning/_tapnet.py b/aeon/classification/deep_learning/_tapnet.py
index 2ad04882..48cc486f 100644
--- a/aeon/classification/deep_learning/_tapnet.py
+++ b/aeon/classification/deep_learning/_tapnet.py
@@ -246,7 +246,7 @@ class TapNetClassifier(BaseDeepClassifier):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -261,7 +261,6 @@ class TapNetClassifier(BaseDeepClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 20,
diff --git a/aeon/classification/dictionary_based/_boss.py b/aeon/classification/dictionary_based/_boss.py
index da821b72..856074b2 100644
--- a/aeon/classification/dictionary_based/_boss.py
+++ b/aeon/classification/dictionary_based/_boss.py
@@ -396,7 +396,7 @@ class BOSSEnsemble(BaseClassifier):
         return correct / train_size
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -418,7 +418,6 @@ class BOSSEnsemble(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/dictionary_based/_cboss.py b/aeon/classification/dictionary_based/_cboss.py
index a649741b..652b4a76 100644
--- a/aeon/classification/dictionary_based/_cboss.py
+++ b/aeon/classification/dictionary_based/_cboss.py
@@ -420,7 +420,7 @@ class ContractableBOSS(BaseClassifier):
         return correct / train_size
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -445,7 +445,6 @@ class ContractableBOSS(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_parameter_samples": 10, "max_ensemble_size": 5}
diff --git a/aeon/classification/dictionary_based/_mrseql.py b/aeon/classification/dictionary_based/_mrseql.py
index 1426844f..b515d31e 100644
--- a/aeon/classification/dictionary_based/_mrseql.py
+++ b/aeon/classification/dictionary_based/_mrseql.py
@@ -106,7 +106,9 @@ class MrSEQLClassifier(BaseClassifier):
         return self.clf_.predict_proba(_X)
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -125,6 +127,5 @@ class MrSEQLClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {}
diff --git a/aeon/classification/dictionary_based/_mrsqm.py b/aeon/classification/dictionary_based/_mrsqm.py
index ce793f54..4cf98014 100644
--- a/aeon/classification/dictionary_based/_mrsqm.py
+++ b/aeon/classification/dictionary_based/_mrsqm.py
@@ -123,7 +123,9 @@ class MrSQMClassifier(BaseClassifier):
         return self.clf_.predict_proba(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -142,7 +144,6 @@ class MrSQMClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "features_per_rep": 50,
diff --git a/aeon/classification/dictionary_based/_muse.py b/aeon/classification/dictionary_based/_muse.py
index f4f0feea..10594821 100644
--- a/aeon/classification/dictionary_based/_muse.py
+++ b/aeon/classification/dictionary_based/_muse.py
@@ -345,7 +345,7 @@ class MUSE(BaseClassifier):
         return X_new
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -360,7 +360,6 @@ class MUSE(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "window_inc": 4,
diff --git a/aeon/classification/dictionary_based/_redcomets.py b/aeon/classification/dictionary_based/_redcomets.py
index f8510945..6ddbb7cf 100644
--- a/aeon/classification/dictionary_based/_redcomets.py
+++ b/aeon/classification/dictionary_based/_redcomets.py
@@ -596,7 +596,7 @@ class REDCOMETS(BaseClassifier):
         return sax_parallel_res
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -610,9 +610,7 @@ class REDCOMETS(BaseClassifier):
         dict
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
-            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
-            instance.``create_test_instance`` uses the first (or only) dictionary in
-            `params``.
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
         """
         return {
             "variant": 3,
diff --git a/aeon/classification/dictionary_based/_tde.py b/aeon/classification/dictionary_based/_tde.py
index 97a98ecb..f8af016b 100644
--- a/aeon/classification/dictionary_based/_tde.py
+++ b/aeon/classification/dictionary_based/_tde.py
@@ -530,7 +530,7 @@ class TemporalDictionaryEnsemble(BaseClassifier):
         return correct / train_size
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -555,7 +555,6 @@ class TemporalDictionaryEnsemble(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/dictionary_based/_weasel.py b/aeon/classification/dictionary_based/_weasel.py
index dee61e3b..03b86a8c 100644
--- a/aeon/classification/dictionary_based/_weasel.py
+++ b/aeon/classification/dictionary_based/_weasel.py
@@ -316,7 +316,7 @@ class WEASEL(BaseClassifier):
         return 1 if self.n_timepoints < 100 else self.window_inc
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -331,7 +331,6 @@ class WEASEL(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "window_inc": 4,
diff --git a/aeon/classification/dictionary_based/_weasel_v2.py b/aeon/classification/dictionary_based/_weasel_v2.py
index 0dd7539b..b8d014a0 100644
--- a/aeon/classification/dictionary_based/_weasel_v2.py
+++ b/aeon/classification/dictionary_based/_weasel_v2.py
@@ -236,7 +236,7 @@ class WEASEL_V2(BaseClassifier):
             return super()._predict_proba(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -251,7 +251,6 @@ class WEASEL_V2(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {"feature_selection": "none"}
 
diff --git a/aeon/classification/distance_based/_elastic_ensemble.py b/aeon/classification/distance_based/_elastic_ensemble.py
index 8bd0d6d3..c2b159f8 100644
--- a/aeon/classification/distance_based/_elastic_ensemble.py
+++ b/aeon/classification/distance_based/_elastic_ensemble.py
@@ -491,7 +491,9 @@ class ElasticEnsemble(BaseClassifier):
             )
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -510,7 +512,6 @@ class ElasticEnsemble(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py
index 870dded9..d0f7144e 100644
--- a/aeon/classification/distance_based/_time_series_neighbors.py
+++ b/aeon/classification/distance_based/_time_series_neighbors.py
@@ -219,7 +219,9 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier):
         return closest_idx, ws
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -234,7 +236,6 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         # non-default distance and algorithm
         params1 = {"distance": "euclidean"}
diff --git a/aeon/classification/early_classification/_probability_threshold.py b/aeon/classification/early_classification/_probability_threshold.py
index f34d8cd1..79d2f498 100644
--- a/aeon/classification/early_classification/_probability_threshold.py
+++ b/aeon/classification/early_classification/_probability_threshold.py
@@ -460,7 +460,7 @@ class ProbabilityThresholdEarlyClassifier(BaseEarlyClassifier):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -479,7 +479,6 @@ class ProbabilityThresholdEarlyClassifier(BaseEarlyClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.classification.feature_based import SummaryClassifier
         from aeon.classification.interval_based import TimeSeriesForestClassifier
diff --git a/aeon/classification/early_classification/_teaser.py b/aeon/classification/early_classification/_teaser.py
index aded30f6..8a01bbba 100644
--- a/aeon/classification/early_classification/_teaser.py
+++ b/aeon/classification/early_classification/_teaser.py
@@ -622,7 +622,7 @@ class TEASER(BaseEarlyClassifier):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -641,7 +641,6 @@ class TEASER(BaseEarlyClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.classification.feature_based import SummaryClassifier
         from aeon.classification.interval_based import TimeSeriesForestClassifier
diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py
index c99475cf..16834220 100644
--- a/aeon/classification/feature_based/_catch22.py
+++ b/aeon/classification/feature_based/_catch22.py
@@ -234,7 +234,7 @@ class Catch22Classifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -253,7 +253,6 @@ class Catch22Classifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/feature_based/_fresh_prince.py b/aeon/classification/feature_based/_fresh_prince.py
index 2760c226..54c4dcb9 100644
--- a/aeon/classification/feature_based/_fresh_prince.py
+++ b/aeon/classification/feature_based/_fresh_prince.py
@@ -200,7 +200,7 @@ class FreshPRINCEClassifier(BaseClassifier):
         return self._tsfresh.fit_transform(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -222,7 +222,6 @@ class FreshPRINCEClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/feature_based/_signature_classifier.py b/aeon/classification/feature_based/_signature_classifier.py
index 445efb7b..88308436 100644
--- a/aeon/classification/feature_based/_signature_classifier.py
+++ b/aeon/classification/feature_based/_signature_classifier.py
@@ -193,7 +193,7 @@ class SignatureClassifier(BaseClassifier):
         return self.pipeline.predict_proba(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -212,7 +212,6 @@ class SignatureClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"estimator": RandomForestClassifier(n_estimators=10)}
diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py
index 43e6b33d..b42c4b6b 100644
--- a/aeon/classification/feature_based/_summary.py
+++ b/aeon/classification/feature_based/_summary.py
@@ -174,7 +174,7 @@ class SummaryClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -193,7 +193,6 @@ class SummaryClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"estimator": RandomForestClassifier(n_estimators=10)}
diff --git a/aeon/classification/feature_based/_tsfresh.py b/aeon/classification/feature_based/_tsfresh.py
index fcb9159f..23d79e44 100644
--- a/aeon/classification/feature_based/_tsfresh.py
+++ b/aeon/classification/feature_based/_tsfresh.py
@@ -220,7 +220,7 @@ class TSFreshClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -239,7 +239,6 @@ class TSFreshClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/hybrid/_hivecote_v1.py b/aeon/classification/hybrid/_hivecote_v1.py
index 71f05fe2..22925487 100644
--- a/aeon/classification/hybrid/_hivecote_v1.py
+++ b/aeon/classification/hybrid/_hivecote_v1.py
@@ -292,7 +292,7 @@ class HIVECOTEV1(BaseClassifier):
         return dists / dists.sum(axis=1, keepdims=True)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -311,7 +311,6 @@ class HIVECOTEV1(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from sklearn.ensemble import RandomForestClassifier
 
diff --git a/aeon/classification/hybrid/_hivecote_v2.py b/aeon/classification/hybrid/_hivecote_v2.py
index 1b77f329..53cd94ef 100644
--- a/aeon/classification/hybrid/_hivecote_v2.py
+++ b/aeon/classification/hybrid/_hivecote_v2.py
@@ -325,7 +325,7 @@ class HIVECOTEV2(BaseClassifier):
         return dists / dists.sum(axis=1, keepdims=True)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -347,7 +347,6 @@ class HIVECOTEV2(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from sklearn.ensemble import RandomForestClassifier
 
diff --git a/aeon/classification/hybrid/_rist.py b/aeon/classification/hybrid/_rist.py
index 2a036cd8..d3db7585 100644
--- a/aeon/classification/hybrid/_rist.py
+++ b/aeon/classification/hybrid/_rist.py
@@ -134,7 +134,7 @@ class RISTClassifier(BaseRIST, BaseClassifier):
     }
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return unit test parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/classification/interval_based/_cif.py b/aeon/classification/interval_based/_cif.py
index 92ee2f3c..a0c91d37 100644
--- a/aeon/classification/interval_based/_cif.py
+++ b/aeon/classification/interval_based/_cif.py
@@ -233,7 +233,7 @@ class CanonicalIntervalForestClassifier(BaseIntervalForest, BaseClassifier):
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -258,7 +258,6 @@ class CanonicalIntervalForestClassifier(BaseIntervalForest, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/classification/interval_based/_drcif.py b/aeon/classification/interval_based/_drcif.py
index af3ed647..650bcf42 100644
--- a/aeon/classification/interval_based/_drcif.py
+++ b/aeon/classification/interval_based/_drcif.py
@@ -260,7 +260,7 @@ class DrCIFClassifier(BaseIntervalForest, BaseClassifier):
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -285,7 +285,6 @@ class DrCIFClassifier(BaseIntervalForest, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/classification/interval_based/_interval_forest.py b/aeon/classification/interval_based/_interval_forest.py
index e7729cee..f1593adf 100644
--- a/aeon/classification/interval_based/_interval_forest.py
+++ b/aeon/classification/interval_based/_interval_forest.py
@@ -225,7 +225,7 @@ class IntervalForestClassifier(BaseIntervalForest, BaseClassifier):
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -250,7 +250,6 @@ class IntervalForestClassifier(BaseIntervalForest, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2}
diff --git a/aeon/classification/interval_based/_interval_pipelines.py b/aeon/classification/interval_based/_interval_pipelines.py
index 5b71b57d..d29044e4 100644
--- a/aeon/classification/interval_based/_interval_pipelines.py
+++ b/aeon/classification/interval_based/_interval_pipelines.py
@@ -211,7 +211,7 @@ class RandomIntervalClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -230,7 +230,6 @@ class RandomIntervalClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.utils.numba.stats import row_mean, row_numba_min
 
@@ -450,7 +449,7 @@ class SupervisedIntervalClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -469,7 +468,6 @@ class SupervisedIntervalClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.utils.numba.stats import row_mean, row_numba_min
 
diff --git a/aeon/classification/interval_based/_rise.py b/aeon/classification/interval_based/_rise.py
index ef3235bd..b298fa59 100644
--- a/aeon/classification/interval_based/_rise.py
+++ b/aeon/classification/interval_based/_rise.py
@@ -195,7 +195,7 @@ class RandomIntervalSpectralEnsembleClassifier(BaseIntervalForest, BaseClassifie
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -221,7 +221,6 @@ class RandomIntervalSpectralEnsembleClassifier(BaseIntervalForest, BaseClassifie
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10}
diff --git a/aeon/classification/interval_based/_rstsf.py b/aeon/classification/interval_based/_rstsf.py
index 19cb902f..9280a4a0 100644
--- a/aeon/classification/interval_based/_rstsf.py
+++ b/aeon/classification/interval_based/_rstsf.py
@@ -172,7 +172,7 @@ class RSTSF(BaseClassifier):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -191,7 +191,6 @@ class RSTSF(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "n_estimators": 2,
diff --git a/aeon/classification/interval_based/_stsf.py b/aeon/classification/interval_based/_stsf.py
index b6bf3c92..f782cb47 100644
--- a/aeon/classification/interval_based/_stsf.py
+++ b/aeon/classification/interval_based/_stsf.py
@@ -186,7 +186,7 @@ class SupervisedTimeSeriesForest(BaseIntervalForest, BaseClassifier):
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -211,7 +211,6 @@ class SupervisedTimeSeriesForest(BaseIntervalForest, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10}
diff --git a/aeon/classification/interval_based/_tsf.py b/aeon/classification/interval_based/_tsf.py
index a624fc99..17ecd5f7 100644
--- a/aeon/classification/interval_based/_tsf.py
+++ b/aeon/classification/interval_based/_tsf.py
@@ -198,7 +198,7 @@ class TimeSeriesForestClassifier(BaseIntervalForest, BaseClassifier):
         return super()._fit_predict_proba(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -223,7 +223,6 @@ class TimeSeriesForestClassifier(BaseIntervalForest, BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10}
diff --git a/aeon/classification/ordinal_classification/_ordinal_tde.py b/aeon/classification/ordinal_classification/_ordinal_tde.py
index 982aab1f..886ff470 100644
--- a/aeon/classification/ordinal_classification/_ordinal_tde.py
+++ b/aeon/classification/ordinal_classification/_ordinal_tde.py
@@ -514,7 +514,7 @@ class OrdinalTDE(BaseClassifier):
         return mae
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -533,7 +533,6 @@ class OrdinalTDE(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/classification/shapelet_based/_ls.py b/aeon/classification/shapelet_based/_ls.py
index 06cc9fbc..1398054a 100644
--- a/aeon/classification/shapelet_based/_ls.py
+++ b/aeon/classification/shapelet_based/_ls.py
@@ -209,7 +209,9 @@ class LearningShapeletClassifier(BaseClassifier):
         return self.clf_.locate(self.transformed_data_)
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -228,6 +230,5 @@ class LearningShapeletClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {"max_iter": 50, "batch_size": 10}
diff --git a/aeon/classification/shapelet_based/_rdst.py b/aeon/classification/shapelet_based/_rdst.py
index 4288686f..cd1756c9 100644
--- a/aeon/classification/shapelet_based/_rdst.py
+++ b/aeon/classification/shapelet_based/_rdst.py
@@ -265,7 +265,9 @@ class RDSTClassifier(BaseClassifier):
             return dists
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -284,6 +286,5 @@ class RDSTClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {"max_shapelets": 20}
diff --git a/aeon/classification/shapelet_based/_stc.py b/aeon/classification/shapelet_based/_stc.py
index 3add6f52..c0ac9df2 100644
--- a/aeon/classification/shapelet_based/_stc.py
+++ b/aeon/classification/shapelet_based/_stc.py
@@ -318,7 +318,9 @@ class ShapeletTransformClassifier(BaseClassifier):
         return self._transformer.fit_transform(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -343,7 +345,6 @@ class ShapeletTransformClassifier(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from sklearn.ensemble import RandomForestClassifier
 
diff --git a/aeon/classification/sklearn/_wrapper.py b/aeon/classification/sklearn/_wrapper.py
index 79362af2..889f1811 100644
--- a/aeon/classification/sklearn/_wrapper.py
+++ b/aeon/classification/sklearn/_wrapper.py
@@ -47,7 +47,7 @@ class SklearnClassifierWrapper(BaseClassifier):
         return self.classifier_.predict_proba(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -62,7 +62,6 @@ class SklearnClassifierWrapper(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "classifier": RandomForestClassifier(n_estimators=5),
diff --git a/aeon/clustering/_clara.py b/aeon/clustering/_clara.py
index 0121922f..4f44f5ad 100644
--- a/aeon/clustering/_clara.py
+++ b/aeon/clustering/_clara.py
@@ -211,7 +211,7 @@ class TimeSeriesCLARA(BaseClusterer):
         return -self.inertia_
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -227,7 +227,6 @@ class TimeSeriesCLARA(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_clarans.py b/aeon/clustering/_clarans.py
index 0d15bc7b..f1c9eff8 100644
--- a/aeon/clustering/_clarans.py
+++ b/aeon/clustering/_clarans.py
@@ -185,7 +185,7 @@ class TimeSeriesCLARANS(TimeSeriesKMedoids):
         self.n_iter_ = 0
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -200,7 +200,6 @@ class TimeSeriesCLARANS(TimeSeriesKMedoids):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_elastic_som.py b/aeon/clustering/_elastic_som.py
index 6b8899e2..e7d7d346 100644
--- a/aeon/clustering/_elastic_som.py
+++ b/aeon/clustering/_elastic_som.py
@@ -382,7 +382,7 @@ class ElasticSOM(BaseClusterer):
         return X[list(range(self.n_clusters))]
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -398,7 +398,6 @@ class ElasticSOM(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py
index c947be4a..550d3894 100644
--- a/aeon/clustering/_k_means.py
+++ b/aeon/clustering/_k_means.py
@@ -408,7 +408,7 @@ class TimeSeriesKMeans(BaseClusterer):
         return curr_pw, curr_labels, curr_inertia, cluster_centres
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -424,7 +424,6 @@ class TimeSeriesKMeans(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py
index 9a0a91ae..1f36f75e 100644
--- a/aeon/clustering/_k_medoids.py
+++ b/aeon/clustering/_k_medoids.py
@@ -541,7 +541,7 @@ class TimeSeriesKMedoids(BaseClusterer):
         return np.array(medoid_idxs)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -557,7 +557,6 @@ class TimeSeriesKMedoids(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_k_sc.py b/aeon/clustering/_k_sc.py
index 24caa8e9..1ace94b2 100644
--- a/aeon/clustering/_k_sc.py
+++ b/aeon/clustering/_k_sc.py
@@ -130,7 +130,7 @@ class KSpectralCentroid(TimeSeriesKMeans):
             self._average_params["max_shift"] = temp_max_shift
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -146,7 +146,6 @@ class KSpectralCentroid(TimeSeriesKMeans):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_k_shape.py b/aeon/clustering/_k_shape.py
index 9bcf4d16..3da2aca0 100644
--- a/aeon/clustering/_k_shape.py
+++ b/aeon/clustering/_k_shape.py
@@ -153,7 +153,7 @@ class TimeSeriesKShape(BaseClusterer):
         return self._tslearn_k_shapes.predict(_X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -169,7 +169,6 @@ class TimeSeriesKShape(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
index 7d323c9a..cdad5803 100644
--- a/aeon/clustering/_k_shapes.py
+++ b/aeon/clustering/_k_shapes.py
@@ -154,7 +154,7 @@ class TimeSeriesKShapes(BaseClusterer):
         return self._tslearn_k_shapes.predict(_X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -170,7 +170,6 @@ class TimeSeriesKShapes(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py
index 23ab8430..6511c6a3 100644
--- a/aeon/clustering/_kernel_k_means.py
+++ b/aeon/clustering/_kernel_k_means.py
@@ -176,7 +176,7 @@ class TimeSeriesKernelKMeans(BaseClusterer):
         return self._tslearn_kernel_k_means.predict(_X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default") -> dict:
+    def _get_test_params(cls, parameter_set="default") -> dict:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -192,7 +192,6 @@ class TimeSeriesKernelKMeans(BaseClusterer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_clusters": 2,
diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 63f9c805..38d3214f 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -67,7 +67,7 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer):
     >>> X_train, y_train = load_unit_test(split="train")
     >>> X_test, y_test = load_unit_test(split="test")
     >>> pipeline = ClustererPipeline(
-    ...     Resizer(length=10), TimeSeriesKMeans.create_test_instance()
+    ...     Resizer(length=10), TimeSeriesKMeans._create_test_instance()
     ... )
     >>> pipeline.fit(X_train, y_train)
     ClustererPipeline(...)
@@ -92,7 +92,7 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer):
         raise NotImplementedError("Pipeline does not support scoring.")
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -107,7 +107,6 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.clustering import TimeSeriesKMeans
         from aeon.transformations.collection import Truncator
@@ -120,5 +119,5 @@ class ClustererPipeline(BaseCollectionPipeline, BaseClusterer):
                 Truncator(truncated_length=5),
                 SevenNumberSummaryTransformer(),
             ],
-            "clusterer": TimeSeriesKMeans.create_test_instance(),
+            "clusterer": TimeSeriesKMeans._create_test_instance(),
         }
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 50089a6b..00758178 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -327,7 +327,7 @@ class AEFCNClusterer(BaseDeepClusterer):
         return self._estimator.score(latent_space)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -346,7 +346,6 @@ class AEFCNClusterer(BaseDeepClusterer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 1,
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index 170e57b2..56d12cb4 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -343,7 +343,7 @@ class AEResNetClusterer(BaseDeepClusterer):
         return self._estimator.score(latent_space)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -362,7 +362,6 @@ class AEResNetClusterer(BaseDeepClusterer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 1,
diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py
index 6c716e24..0b6b2e32 100644
--- a/aeon/clustering/feature_based/_catch22.py
+++ b/aeon/clustering/feature_based/_catch22.py
@@ -218,7 +218,7 @@ class Catch22Clusterer(BaseClusterer):
         raise NotImplementedError("Catch22Clusterer does not support scoring.")
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -233,7 +233,6 @@ class Catch22Clusterer(BaseClusterer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "features": (
diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py
index 2f021c78..7d30b396 100644
--- a/aeon/clustering/feature_based/_tsfresh.py
+++ b/aeon/clustering/feature_based/_tsfresh.py
@@ -215,7 +215,7 @@ class TSFreshClusterer(BaseClusterer):
         raise NotImplementedError("TSFreshClusterer does not support scoring.")
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default"):
+    def _get_test_params(cls, parameter_set: str = "default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -230,7 +230,6 @@ class TSFreshClusterer(BaseClusterer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "default_fc_parameters": "minimal",
diff --git a/aeon/regression/compose/_ensemble.py b/aeon/regression/compose/_ensemble.py
index c0b7d53d..838456a9 100644
--- a/aeon/regression/compose/_ensemble.py
+++ b/aeon/regression/compose/_ensemble.py
@@ -106,7 +106,7 @@ class RegressorEnsemble(BaseCollectionEnsemble, BaseRegressor):
             return reg
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -121,12 +121,11 @@ class RegressorEnsemble(BaseCollectionEnsemble, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "regressors": [
-                KNeighborsTimeSeriesRegressor.create_test_instance(),
-                DummyRegressor.create_test_instance(),
+                KNeighborsTimeSeriesRegressor._create_test_instance(),
+                DummyRegressor._create_test_instance(),
             ],
             "weights": [2, 1],
         }
diff --git a/aeon/regression/compose/_pipeline.py b/aeon/regression/compose/_pipeline.py
index 00bb1d4d..764cc714 100644
--- a/aeon/regression/compose/_pipeline.py
+++ b/aeon/regression/compose/_pipeline.py
@@ -82,7 +82,7 @@ class RegressorPipeline(BaseCollectionPipeline, BaseRegressor):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -97,7 +97,6 @@ class RegressorPipeline(BaseCollectionPipeline, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.regression.distance_based import KNeighborsTimeSeriesRegressor
         from aeon.transformations.collection import Truncator
diff --git a/aeon/regression/convolution_based/_minirocket.py b/aeon/regression/convolution_based/_minirocket.py
index 2c723b5f..3e79965b 100644
--- a/aeon/regression/convolution_based/_minirocket.py
+++ b/aeon/regression/convolution_based/_minirocket.py
@@ -146,7 +146,7 @@ class MiniRocketRegressor(BaseRegressor):
         return self.pipeline_.predict(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/regression/convolution_based/_multirocket.py b/aeon/regression/convolution_based/_multirocket.py
index 4218434a..4cdf782c 100644
--- a/aeon/regression/convolution_based/_multirocket.py
+++ b/aeon/regression/convolution_based/_multirocket.py
@@ -152,7 +152,7 @@ class MultiRocketRegressor(BaseRegressor):
         return self.pipeline_.predict(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/regression/convolution_based/_rocket.py b/aeon/regression/convolution_based/_rocket.py
index 92c2ae7b..5bee6b51 100644
--- a/aeon/regression/convolution_based/_rocket.py
+++ b/aeon/regression/convolution_based/_rocket.py
@@ -146,7 +146,7 @@ class RocketRegressor(BaseRegressor):
         return self.pipeline_.predict(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/regression/deep_learning/_cnn.py b/aeon/regression/deep_learning/_cnn.py
index 58f0f4a5..c636c700 100644
--- a/aeon/regression/deep_learning/_cnn.py
+++ b/aeon/regression/deep_learning/_cnn.py
@@ -312,7 +312,7 @@ class TimeCNNRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -331,7 +331,6 @@ class TimeCNNRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_encoder.py b/aeon/regression/deep_learning/_encoder.py
index 4b73047c..2183d5ee 100644
--- a/aeon/regression/deep_learning/_encoder.py
+++ b/aeon/regression/deep_learning/_encoder.py
@@ -298,7 +298,7 @@ class EncoderRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -317,7 +317,6 @@ class EncoderRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 8,
diff --git a/aeon/regression/deep_learning/_fcn.py b/aeon/regression/deep_learning/_fcn.py
index 374feb5b..361bc2ee 100644
--- a/aeon/regression/deep_learning/_fcn.py
+++ b/aeon/regression/deep_learning/_fcn.py
@@ -306,7 +306,7 @@ class FCNRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -325,7 +325,6 @@ class FCNRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_inception_time.py b/aeon/regression/deep_learning/_inception_time.py
index 19493ac6..bc13532a 100644
--- a/aeon/regression/deep_learning/_inception_time.py
+++ b/aeon/regression/deep_learning/_inception_time.py
@@ -327,7 +327,7 @@ class InceptionTimeRegressor(BaseRegressor):
         return ypreds
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -346,7 +346,6 @@ class InceptionTimeRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_regressors": 1,
@@ -699,7 +698,7 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -718,7 +717,6 @@ class IndividualInceptionRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_lite_time.py b/aeon/regression/deep_learning/_lite_time.py
index 56e33849..f0712a5a 100644
--- a/aeon/regression/deep_learning/_lite_time.py
+++ b/aeon/regression/deep_learning/_lite_time.py
@@ -240,7 +240,7 @@ class LITETimeRegressor(BaseRegressor):
         return vals
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -259,7 +259,6 @@ class LITETimeRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_regressors": 1,
@@ -549,7 +548,7 @@ class IndividualLITERegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -568,7 +567,6 @@ class IndividualLITERegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_mlp.py b/aeon/regression/deep_learning/_mlp.py
index cb9907fe..d593eb7b 100644
--- a/aeon/regression/deep_learning/_mlp.py
+++ b/aeon/regression/deep_learning/_mlp.py
@@ -264,7 +264,7 @@ class MLPRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -283,7 +283,6 @@ class MLPRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_resnet.py b/aeon/regression/deep_learning/_resnet.py
index 48f2d3c5..7592d4e6 100644
--- a/aeon/regression/deep_learning/_resnet.py
+++ b/aeon/regression/deep_learning/_resnet.py
@@ -328,7 +328,7 @@ class ResNetRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -347,7 +347,6 @@ class ResNetRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param = {
             "n_epochs": 10,
diff --git a/aeon/regression/deep_learning/_tapnet.py b/aeon/regression/deep_learning/_tapnet.py
index 4a03f02c..09dd2921 100644
--- a/aeon/regression/deep_learning/_tapnet.py
+++ b/aeon/regression/deep_learning/_tapnet.py
@@ -250,7 +250,7 @@ class TapNetRegressor(BaseDeepRegressor):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -269,7 +269,6 @@ class TapNetRegressor(BaseDeepRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         param1 = {
             "n_epochs": 10,
diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py
index 510fa30a..5c065939 100644
--- a/aeon/regression/distance_based/_time_series_neighbors.py
+++ b/aeon/regression/distance_based/_time_series_neighbors.py
@@ -183,7 +183,9 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor):
         return closest_idx, ws
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> Union[dict, list[dict]]:
+    def _get_test_params(
+        cls, parameter_set: str = "default"
+    ) -> Union[dict, list[dict]]:
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -198,7 +200,6 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         # non-default distance and algorithm
         params1 = {"distance": "euclidean"}
diff --git a/aeon/regression/feature_based/_catch22.py b/aeon/regression/feature_based/_catch22.py
index 579ec874..87e158ca 100644
--- a/aeon/regression/feature_based/_catch22.py
+++ b/aeon/regression/feature_based/_catch22.py
@@ -195,7 +195,7 @@ class Catch22Regressor(BaseRegressor):
         return self._estimator.predict(self._transformer.transform(X))
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -214,7 +214,6 @@ class Catch22Regressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/regression/feature_based/_fresh_prince.py b/aeon/regression/feature_based/_fresh_prince.py
index 029e7535..491e5a3f 100644
--- a/aeon/regression/feature_based/_fresh_prince.py
+++ b/aeon/regression/feature_based/_fresh_prince.py
@@ -180,7 +180,7 @@ class FreshPRINCERegressor(BaseRegressor):
         return self._tsfresh.fit_transform(X, y)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -202,7 +202,6 @@ class FreshPRINCERegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/regression/feature_based/_summary.py b/aeon/regression/feature_based/_summary.py
index 4e2f89ee..eba030d9 100644
--- a/aeon/regression/feature_based/_summary.py
+++ b/aeon/regression/feature_based/_summary.py
@@ -145,7 +145,7 @@ class SummaryRegressor(BaseRegressor):
         return self._estimator.predict(self._transformer.transform(X))
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -164,7 +164,6 @@ class SummaryRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"estimator": RandomForestRegressor(n_estimators=10)}
diff --git a/aeon/regression/feature_based/_tsfresh.py b/aeon/regression/feature_based/_tsfresh.py
index 8ed45eff..8d34eaaa 100644
--- a/aeon/regression/feature_based/_tsfresh.py
+++ b/aeon/regression/feature_based/_tsfresh.py
@@ -186,7 +186,7 @@ class TSFreshRegressor(BaseRegressor):
         return self._estimator.predict(self._transformer.transform(X))
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -205,7 +205,6 @@ class TSFreshRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/regression/hybrid/_rist.py b/aeon/regression/hybrid/_rist.py
index e96beb24..f7471ef4 100644
--- a/aeon/regression/hybrid/_rist.py
+++ b/aeon/regression/hybrid/_rist.py
@@ -126,7 +126,7 @@ class RISTRegressor(BaseRIST, BaseRegressor):
     }
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return unit test parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/regression/interval_based/_cif.py b/aeon/regression/interval_based/_cif.py
index 34fede19..61b02906 100644
--- a/aeon/regression/interval_based/_cif.py
+++ b/aeon/regression/interval_based/_cif.py
@@ -194,7 +194,7 @@ class CanonicalIntervalForestRegressor(BaseIntervalForest, BaseRegressor):
             self.set_tags(**{"python_dependencies": "pycatch22"})
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -216,7 +216,6 @@ class CanonicalIntervalForestRegressor(BaseIntervalForest, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/regression/interval_based/_drcif.py b/aeon/regression/interval_based/_drcif.py
index f76f3871..15254796 100644
--- a/aeon/regression/interval_based/_drcif.py
+++ b/aeon/regression/interval_based/_drcif.py
@@ -220,7 +220,7 @@ class DrCIFRegressor(BaseIntervalForest, BaseRegressor):
             self.set_tags(**{"python_dependencies": d})
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -242,7 +242,6 @@ class DrCIFRegressor(BaseIntervalForest, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2, "att_subsample_size": 4}
diff --git a/aeon/regression/interval_based/_interval_forest.py b/aeon/regression/interval_based/_interval_forest.py
index 746d2249..3ddc52de 100644
--- a/aeon/regression/interval_based/_interval_forest.py
+++ b/aeon/regression/interval_based/_interval_forest.py
@@ -201,7 +201,7 @@ class IntervalForestRegressor(BaseIntervalForest, BaseRegressor):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -223,7 +223,6 @@ class IntervalForestRegressor(BaseIntervalForest, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10, "n_intervals": 2}
diff --git a/aeon/regression/interval_based/_interval_pipelines.py b/aeon/regression/interval_based/_interval_pipelines.py
index 04cce958..42ceb467 100644
--- a/aeon/regression/interval_based/_interval_pipelines.py
+++ b/aeon/regression/interval_based/_interval_pipelines.py
@@ -183,7 +183,7 @@ class RandomIntervalRegressor(BaseRegressor):
         return self._estimator.predict(self._transformer.transform(X))
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -202,7 +202,6 @@ class RandomIntervalRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.utils.numba.stats import row_mean, row_numba_min
 
diff --git a/aeon/regression/interval_based/_rise.py b/aeon/regression/interval_based/_rise.py
index 82d317e6..40506552 100644
--- a/aeon/regression/interval_based/_rise.py
+++ b/aeon/regression/interval_based/_rise.py
@@ -162,7 +162,7 @@ class RandomIntervalSpectralEnsembleRegressor(BaseIntervalForest, BaseRegressor)
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -185,7 +185,6 @@ class RandomIntervalSpectralEnsembleRegressor(BaseIntervalForest, BaseRegressor)
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         if parameter_set == "results_comparison":
             return {"n_estimators": 10}
diff --git a/aeon/regression/interval_based/_tsf.py b/aeon/regression/interval_based/_tsf.py
index 1a82635c..b01575f0 100644
--- a/aeon/regression/interval_based/_tsf.py
+++ b/aeon/regression/interval_based/_tsf.py
@@ -162,7 +162,7 @@ class TimeSeriesForestRegressor(BaseIntervalForest, BaseRegressor):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -181,7 +181,6 @@ class TimeSeriesForestRegressor(BaseIntervalForest, BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "n_estimators": 2,
diff --git a/aeon/regression/shapelet_based/_rdst.py b/aeon/regression/shapelet_based/_rdst.py
index 6f0a0b9b..f8f27773 100644
--- a/aeon/regression/shapelet_based/_rdst.py
+++ b/aeon/regression/shapelet_based/_rdst.py
@@ -218,7 +218,7 @@ class RDSTRegressor(BaseRegressor):
         return self._estimator.predict(X_t)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -237,6 +237,5 @@ class RDSTRegressor(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {"max_shapelets": 20}
diff --git a/aeon/regression/sklearn/_wrapper.py b/aeon/regression/sklearn/_wrapper.py
index da8b37fa..caf00f15 100644
--- a/aeon/regression/sklearn/_wrapper.py
+++ b/aeon/regression/sklearn/_wrapper.py
@@ -44,7 +44,7 @@ class SklearnRegressorWrapper(BaseRegressor):
         return self.regressor_.predict(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -59,7 +59,6 @@ class SklearnRegressorWrapper(BaseRegressor):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return {
             "regressor": RandomForestRegressor(n_estimators=5),
diff --git a/aeon/segmentation/_binseg.py b/aeon/segmentation/_binseg.py
index ad54b047..af649590 100644
--- a/aeon/segmentation/_binseg.py
+++ b/aeon/segmentation/_binseg.py
@@ -124,7 +124,7 @@ class BinSegSegmenter(BaseSegmenter):
         return pd.IntervalIndex.from_arrays(start, end)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -139,6 +139,5 @@ class BinSegSegmenter(BaseSegmenter):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {"n_cps": 1}
diff --git a/aeon/segmentation/_clasp.py b/aeon/segmentation/_clasp.py
index 4aed2f86..46dbc190 100644
--- a/aeon/segmentation/_clasp.py
+++ b/aeon/segmentation/_clasp.py
@@ -301,7 +301,7 @@ class ClaSPSegmenter(BaseSegmenter):
         return pd.IntervalIndex.from_arrays(start, end)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -316,6 +316,5 @@ class ClaSPSegmenter(BaseSegmenter):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {"period_length": 5, "n_cps": 1}
diff --git a/aeon/segmentation/_eagglo.py b/aeon/segmentation/_eagglo.py
index 8ffa22cc..d482c47d 100644
--- a/aeon/segmentation/_eagglo.py
+++ b/aeon/segmentation/_eagglo.py
@@ -350,7 +350,7 @@ class EAggloSegmenter(BaseSegmenter):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> list[dict]:
+    def _get_test_params(cls, parameter_set: str = "default") -> list[dict]:
         """Test parameters."""
         return [
             {"alpha": 1.0, "penalty": None},
diff --git a/aeon/segmentation/_fluss.py b/aeon/segmentation/_fluss.py
index 261044f9..de32b9ed 100644
--- a/aeon/segmentation/_fluss.py
+++ b/aeon/segmentation/_fluss.py
@@ -137,7 +137,7 @@ class FLUSSSegmenter(BaseSegmenter):
         return pd.IntervalIndex.from_arrays(start, end)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -152,6 +152,5 @@ class FLUSSSegmenter(BaseSegmenter):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {"period_length": 5, "n_regimes": 2}
diff --git a/aeon/segmentation/_ggs.py b/aeon/segmentation/_ggs.py
index c423dd28..6fef5773 100644
--- a/aeon/segmentation/_ggs.py
+++ b/aeon/segmentation/_ggs.py
@@ -514,7 +514,7 @@ class GreedyGaussianSegmenter(BaseSegmenter):
         return labels
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """
         Return testing parameter settings for the estimator.
 
diff --git a/aeon/segmentation/_hidalgo.py b/aeon/segmentation/_hidalgo.py
index ae34ac99..c70010b7 100644
--- a/aeon/segmentation/_hidalgo.py
+++ b/aeon/segmentation/_hidalgo.py
@@ -653,7 +653,7 @@ class HidalgoSegmenter(BaseSegmenter):
         return self._Z
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -671,7 +671,6 @@ class HidalgoSegmenter(BaseSegmenter):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "metric": "euclidean",
diff --git a/aeon/segmentation/_hmm.py b/aeon/segmentation/_hmm.py
index 49c8b277..6b829603 100644
--- a/aeon/segmentation/_hmm.py
+++ b/aeon/segmentation/_hmm.py
@@ -386,7 +386,7 @@ class HMMSegmenter(BaseSegmenter):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/segmentation/_igts.py b/aeon/segmentation/_igts.py
index ba3c2561..20d63243 100644
--- a/aeon/segmentation/_igts.py
+++ b/aeon/segmentation/_igts.py
@@ -389,7 +389,7 @@ class InformationGainSegmenter(BaseSegmenter):
         return self._igts.__repr__()
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/transformations/collection/_acf.py b/aeon/transformations/collection/_acf.py
index 24c97676..d3110066 100644
--- a/aeon/transformations/collection/_acf.py
+++ b/aeon/transformations/collection/_acf.py
@@ -114,7 +114,7 @@ class AutocorrelationFunctionTransformer(BaseCollectionTransformer):
         return X_t
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -129,7 +129,6 @@ class AutocorrelationFunctionTransformer(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "n_lags": 4,
diff --git a/aeon/transformations/collection/_ar_coefficient.py b/aeon/transformations/collection/_ar_coefficient.py
index 45b96257..4a483d9a 100644
--- a/aeon/transformations/collection/_ar_coefficient.py
+++ b/aeon/transformations/collection/_ar_coefficient.py
@@ -89,7 +89,7 @@ class ARCoefficientTransformer(BaseCollectionTransformer):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -104,7 +104,6 @@ class ARCoefficientTransformer(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return {
             "order": 4,
diff --git a/aeon/transformations/collection/_broadcaster.py b/aeon/transformations/collection/_broadcaster.py
index 500ca1d0..742e24d2 100644
--- a/aeon/transformations/collection/_broadcaster.py
+++ b/aeon/transformations/collection/_broadcaster.py
@@ -145,7 +145,7 @@ class SeriesToCollectionBroadcaster(BaseCollectionTransformer):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -160,7 +160,6 @@ class SeriesToCollectionBroadcaster(BaseCollectionTransformer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.testing.mock_estimators._mock_series_transformers import (
             MockUnivariateSeriesTransformer,
diff --git a/aeon/transformations/collection/_resize.py b/aeon/transformations/collection/_resize.py
index c8189300..abfd6969 100644
--- a/aeon/transformations/collection/_resize.py
+++ b/aeon/transformations/collection/_resize.py
@@ -83,7 +83,7 @@ class Resizer(BaseCollectionTransformer):
         return np.array(Xt)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Returns
@@ -92,7 +92,6 @@ class Resizer(BaseCollectionTransformer):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         params = {"length": 10}
         return params
diff --git a/aeon/transformations/collection/_truncate.py b/aeon/transformations/collection/_truncate.py
index 8818b138..2c20c1e0 100644
--- a/aeon/transformations/collection/_truncate.py
+++ b/aeon/transformations/collection/_truncate.py
@@ -105,7 +105,7 @@ class Truncator(BaseCollectionTransformer):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -121,7 +121,6 @@ class Truncator(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {"truncated_length": 5}
         return params
diff --git a/aeon/transformations/collection/channel_selection/_channel_scorer.py b/aeon/transformations/collection/channel_selection/_channel_scorer.py
index e4289f66..4306cdfe 100644
--- a/aeon/transformations/collection/channel_selection/_channel_scorer.py
+++ b/aeon/transformations/collection/channel_selection/_channel_scorer.py
@@ -129,7 +129,7 @@ class ChannelScorer(BaseChannelSelector):
         return self
 
     @classmethod
-    def get_test_params(cls, parameter_set: str = "default") -> TypingDict[str, any]:
+    def _get_test_params(cls, parameter_set: str = "default") -> TypingDict[str, any]:
         """Return testing parameter settings for the estimator.
 
         Parameters
diff --git a/aeon/transformations/collection/compose/_pipeline.py b/aeon/transformations/collection/compose/_pipeline.py
index 3f66b2c4..a20f198e 100644
--- a/aeon/transformations/collection/compose/_pipeline.py
+++ b/aeon/transformations/collection/compose/_pipeline.py
@@ -76,7 +76,7 @@ class CollectionTransformerPipeline(BaseCollectionPipeline, BaseCollectionTransf
         super().__init__(transformers=transformers, _estimator=None)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -91,7 +91,6 @@ class CollectionTransformerPipeline(BaseCollectionPipeline, BaseCollectionTransf
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         from aeon.transformations.collection import Truncator
         from aeon.transformations.collection.feature_based import (
diff --git a/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py b/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
index fbfd5d18..547c077a 100644
--- a/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
+++ b/aeon/transformations/collection/convolution_based/rocketGPU/_rocket_gpu.py
@@ -226,7 +226,7 @@ class ROCKETGPU(BaseROCKETGPU):
         return output_rocket
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the transformer.
 
         Parameters
@@ -242,7 +242,6 @@ class ROCKETGPU(BaseROCKETGPU):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {
             "n_filters": 5,
diff --git a/aeon/transformations/collection/dictionary_based/_paa.py b/aeon/transformations/collection/dictionary_based/_paa.py
index 2e8690e6..f9efae75 100644
--- a/aeon/transformations/collection/dictionary_based/_paa.py
+++ b/aeon/transformations/collection/dictionary_based/_paa.py
@@ -124,7 +124,7 @@ class PAA(BaseCollectionTransformer):
             return X_inverse_paa
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -140,7 +140,6 @@ class PAA(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {"n_segments": 10}
         return params
diff --git a/aeon/transformations/collection/dictionary_based/_sax.py b/aeon/transformations/collection/dictionary_based/_sax.py
index b5f86c5d..fe49423b 100644
--- a/aeon/transformations/collection/dictionary_based/_sax.py
+++ b/aeon/transformations/collection/dictionary_based/_sax.py
@@ -229,7 +229,7 @@ class SAX(BaseCollectionTransformer):
         return breakpoints, breakpoints_mid
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -245,7 +245,6 @@ class SAX(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {"n_segments": 10, "alphabet_size": 8}
         return params
diff --git a/aeon/transformations/collection/dictionary_based/_sfa.py b/aeon/transformations/collection/dictionary_based/_sfa.py
index 8a67793c..49476483 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa.py
@@ -1151,7 +1151,7 @@ class SFA(BaseCollectionTransformer):
         return letters
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -1167,7 +1167,6 @@ class SFA(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         # small window size for testing
         params = {"window_size": 4}
diff --git a/aeon/transformations/collection/dictionary_based/_sfa_fast.py b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
index ede46bdb..78e3a18b 100644
--- a/aeon/transformations/collection/dictionary_based/_sfa_fast.py
+++ b/aeon/transformations/collection/dictionary_based/_sfa_fast.py
@@ -690,7 +690,7 @@ class SFAFast(BaseCollectionTransformer):
         )
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -705,7 +705,6 @@ class SFAFast(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         # small window size for testing
         params = {
diff --git a/aeon/transformations/collection/feature_based/_tsfresh.py b/aeon/transformations/collection/feature_based/_tsfresh.py
index c9bb8150..4e027332 100644
--- a/aeon/transformations/collection/feature_based/_tsfresh.py
+++ b/aeon/transformations/collection/feature_based/_tsfresh.py
@@ -302,7 +302,7 @@ class TSFreshFeatureExtractor(_TSFreshFeatureExtractor):
         return Xt.to_numpy()
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -318,7 +318,6 @@ class TSFreshFeatureExtractor(_TSFreshFeatureExtractor):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         features_to_calc = [
             "dim_0__quantile__q_0.6",
@@ -665,7 +664,7 @@ class TSFreshRelevantFeatureExtractor(_TSFreshFeatureExtractor):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -681,7 +680,6 @@ class TSFreshRelevantFeatureExtractor(_TSFreshFeatureExtractor):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {
             "default_fc_parameters": "efficient",
diff --git a/aeon/transformations/collection/interval_based/_random_intervals.py b/aeon/transformations/collection/interval_based/_random_intervals.py
index c43b01f5..afcae013 100644
--- a/aeon/transformations/collection/interval_based/_random_intervals.py
+++ b/aeon/transformations/collection/interval_based/_random_intervals.py
@@ -475,7 +475,7 @@ class RandomIntervals(BaseCollectionTransformer):
         return True
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -490,7 +490,6 @@ class RandomIntervals(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         if parameter_set == "results_comparison":
             return {"n_intervals": 3}
diff --git a/aeon/transformations/collection/interval_based/_supervised_intervals.py b/aeon/transformations/collection/interval_based/_supervised_intervals.py
index 5f787e6f..fcdc1faf 100644
--- a/aeon/transformations/collection/interval_based/_supervised_intervals.py
+++ b/aeon/transformations/collection/interval_based/_supervised_intervals.py
@@ -545,7 +545,7 @@ class SupervisedIntervals(BaseCollectionTransformer):
         return True
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -560,7 +560,6 @@ class SupervisedIntervals(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         if parameter_set == "results_comparison":
             return {
diff --git a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
index b18c5f25..2d47bc42 100644
--- a/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
+++ b/aeon/transformations/collection/shapelet_based/_dilated_shapelet_transform.py
@@ -346,7 +346,7 @@ class RandomDilatedShapeletTransform(BaseCollectionTransformer):
             self.threshold_percentiles_ = np.asarray(self.threshold_percentiles_)
 
     @classmethod
-    def get_test_params(
+    def _get_test_params(
         cls, parameter_set: str = "default"
     ) -> "Union[Dict, TypingList[Dict]]":
         """Return testing parameter settings for the estimator.
@@ -364,7 +364,6 @@ class RandomDilatedShapeletTransform(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         if parameter_set == "default":
             params = {"max_shapelets": 10}
diff --git a/aeon/transformations/collection/shapelet_based/_shapelet_transform.py b/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
index 34c47850..bed9582d 100644
--- a/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
+++ b/aeon/transformations/collection/shapelet_based/_shapelet_transform.py
@@ -395,7 +395,7 @@ class RandomShapeletTransform(BaseCollectionTransformer):
         return output
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -410,7 +410,6 @@ class RandomShapeletTransform(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         if parameter_set == "results_comparison":
             return {"max_shapelets": 10, "n_shapelet_samples": 500}
diff --git a/aeon/transformations/collection/signature_based/_signature_method.py b/aeon/transformations/collection/signature_based/_signature_method.py
index 92767c97..7255defe 100644
--- a/aeon/transformations/collection/signature_based/_signature_method.py
+++ b/aeon/transformations/collection/signature_based/_signature_method.py
@@ -98,7 +98,7 @@ class SignatureTransformer(BaseCollectionTransformer):
         return self.signature_method.transform(X)
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -114,7 +114,6 @@ class SignatureTransformer(BaseCollectionTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {
             "augmentation_list": ("basepoint", "addtime"),
diff --git a/aeon/transformations/series/_acf.py b/aeon/transformations/series/_acf.py
index 56d4dab1..1e354cba 100644
--- a/aeon/transformations/series/_acf.py
+++ b/aeon/transformations/series/_acf.py
@@ -111,7 +111,7 @@ class AutoCorrelationSeriesTransformer(BaseSeriesTransformer):
         return X_t
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -127,7 +127,6 @@ class AutoCorrelationSeriesTransformer(BaseSeriesTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return [{}, {"n_lags": 1}]
 
@@ -235,7 +234,7 @@ class StatsModelsACF(BaseSeriesTransformer):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -251,7 +250,6 @@ class StatsModelsACF(BaseSeriesTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return [{}, {"n_lags": 1}]
 
@@ -350,7 +348,7 @@ class StatsModelsPACF(BaseSeriesTransformer):
         return Xt
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -366,6 +364,5 @@ class StatsModelsPACF(BaseSeriesTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         return [{}, {"n_lags": 1}]
diff --git a/aeon/transformations/series/_bkfilter.py b/aeon/transformations/series/_bkfilter.py
index d213e67d..62440d1a 100644
--- a/aeon/transformations/series/_bkfilter.py
+++ b/aeon/transformations/series/_bkfilter.py
@@ -103,7 +103,7 @@ class BKFilter(BaseSeriesTransformer):
         return XTr
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -119,7 +119,6 @@ class BKFilter(BaseSeriesTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         params = {"low": 6, "high": 24, "K": 12}
         return params
diff --git a/aeon/transformations/series/_scaled_logit.py b/aeon/transformations/series/_scaled_logit.py
index 7466b2f1..be483b99 100644
--- a/aeon/transformations/series/_scaled_logit.py
+++ b/aeon/transformations/series/_scaled_logit.py
@@ -137,7 +137,7 @@ class ScaledLogitSeriesTransformer(BaseSeriesTransformer):
         return X_inv_transformed
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -153,7 +153,6 @@ class ScaledLogitSeriesTransformer(BaseSeriesTransformer):
             Parameters to create testing instances of the class
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`
         """
         test_params = [
             {"lower_bound": None, "upper_bound": None},


Test patch:
diff --git a/aeon/classification/interval_based/tests/test_interval_pipelines.py b/aeon/classification/interval_based/tests/test_interval_pipelines.py
index ca4b4dfb..4e0f120f 100644
--- a/aeon/classification/interval_based/tests/test_interval_pipelines.py
+++ b/aeon/classification/interval_based/tests/test_interval_pipelines.py
@@ -27,9 +27,11 @@ def test_random_interval_classifier(cls):
 
 def test_parameter_sets():
     """Test results comparison parameter sets."""
-    paras = SupervisedIntervalClassifier.get_test_params(
+    paras = SupervisedIntervalClassifier._get_test_params(
         parameter_set="results_comparison"
     )
     assert paras["n_intervals"] == 2
-    paras = RandomIntervalClassifier.get_test_params(parameter_set="results_comparison")
+    paras = RandomIntervalClassifier._get_test_params(
+        parameter_set="results_comparison"
+    )
     assert paras["n_intervals"] == 3
diff --git a/aeon/classification/tests/test_sklearn_compatability.py b/aeon/classification/tests/test_sklearn_compatability.py
index 0e084fe2..e6b66684 100644
--- a/aeon/classification/tests/test_sklearn_compatability.py
+++ b/aeon/classification/tests/test_sklearn_compatability.py
@@ -61,18 +61,18 @@ COMPOSITE_ESTIMATORS = [
     Pipeline(
         [
             ("transform", Resizer(length=10)),
-            ("clf", CanonicalIntervalForestClassifier.create_test_instance()),
+            ("clf", CanonicalIntervalForestClassifier._create_test_instance()),
         ]
     ),
     VotingClassifier(
         estimators=[
-            ("clf1", CanonicalIntervalForestClassifier.create_test_instance()),
-            ("clf2", CanonicalIntervalForestClassifier.create_test_instance()),
-            ("clf3", CanonicalIntervalForestClassifier.create_test_instance()),
+            ("clf1", CanonicalIntervalForestClassifier._create_test_instance()),
+            ("clf2", CanonicalIntervalForestClassifier._create_test_instance()),
+            ("clf3", CanonicalIntervalForestClassifier._create_test_instance()),
         ]
     ),
     CalibratedClassifierCV(
-        estimator=CanonicalIntervalForestClassifier.create_test_instance(),
+        estimator=CanonicalIntervalForestClassifier._create_test_instance(),
         cv=2,
     ),
 ]
@@ -80,7 +80,7 @@ COMPOSITE_ESTIMATORS = [
 
 def test_sklearn_cross_validation():
     """Test sklearn cross-validation works with aeon data and classifiers."""
-    clf = CanonicalIntervalForestClassifier.create_test_instance()
+    clf = CanonicalIntervalForestClassifier._create_test_instance()
     X, y = make_example_3d_numpy(n_cases=20, n_channels=2, n_timepoints=30)
     scores = cross_val_score(clf, X, y=y, cv=KFold(n_splits=2))
     assert isinstance(scores, np.ndarray)
@@ -99,7 +99,7 @@ def test_sklearn_cross_validation_iterators(cross_validation_method):
 @pytest.mark.parametrize("parameter_tuning_method", PARAMETER_TUNING_METHODS)
 def test_sklearn_parameter_tuning(parameter_tuning_method):
     """Test if sklearn parameter tuners can handle aeon data and classifiers."""
-    clf = CanonicalIntervalForestClassifier.create_test_instance()
+    clf = CanonicalIntervalForestClassifier._create_test_instance()
     param_grid = {"n_intervals": [2, 3], "att_subsample_size": [2, 3]}
     X, y = make_example_3d_numpy(n_cases=20, n_channels=2, n_timepoints=30)
 
diff --git a/aeon/clustering/compose/tests/test_pipeline.py b/aeon/clustering/compose/tests/test_pipeline.py
index 5225e1d2..a263e187 100644
--- a/aeon/clustering/compose/tests/test_pipeline.py
+++ b/aeon/clustering/compose/tests/test_pipeline.py
@@ -44,7 +44,7 @@ def test_clusterer_pipeline(transformers):
     X_train, y_train = make_example_3d_numpy(n_cases=10, n_timepoints=12)
     X_test, _ = make_example_3d_numpy(n_cases=10, n_timepoints=12)
 
-    c = TimeSeriesKMeans.create_test_instance()
+    c = TimeSeriesKMeans._create_test_instance()
     pipeline = ClustererPipeline(transformers=transformers, clusterer=c)
     pipeline.fit(X_train, y_train)
     c.fit(X_train, y_train)
@@ -240,7 +240,7 @@ def test_multivariate_tag_inference():
     assert not t3.get_tag("capability:multivariate")
 
     # todo revisit with mock clusterer
-    c1 = TimeSeriesKMeans.create_test_instance()
+    c1 = TimeSeriesKMeans._create_test_instance()
     # c2 = ContractableBOSS(n_parameter_samples=5, max_ensemble_size=3)
     c3 = KMeans(n_clusters=2, max_iter=3, random_state=0)
 
diff --git a/aeon/pipeline/tests/test_make_pipeline.py b/aeon/pipeline/tests/test_make_pipeline.py
index ea8d5df6..2e52134d 100644
--- a/aeon/pipeline/tests/test_make_pipeline.py
+++ b/aeon/pipeline/tests/test_make_pipeline.py
@@ -23,7 +23,7 @@ from aeon.transformations.collection.feature_based import SevenNumberSummaryTran
         [SevenNumberSummaryTransformer(), RandomForestClassifier(n_estimators=2)],
         [Padder(pad_length=15), DummyRegressor()],
         [SevenNumberSummaryTransformer(), RandomForestRegressor(n_estimators=2)],
-        [Padder(pad_length=15), TimeSeriesKMeans.create_test_instance()],
+        [Padder(pad_length=15), TimeSeriesKMeans._create_test_instance()],
         [SevenNumberSummaryTransformer(), KMeans(n_clusters=2, max_iter=3)],
         [Padder(pad_length=15), SevenNumberSummaryTransformer()],
         [Padder(pad_length=15), Tabularizer(), StandardScaler()],
diff --git a/aeon/regression/interval_based/tests/test_cif.py b/aeon/regression/interval_based/tests/test_cif.py
index 83deef37..db5d811f 100644
--- a/aeon/regression/interval_based/tests/test_cif.py
+++ b/aeon/regression/interval_based/tests/test_cif.py
@@ -8,7 +8,7 @@ def test_cif():
     dr = CanonicalIntervalForestRegressor(use_pycatch22=True)
     d = dr.get_tag("python_dependencies")
     assert d == "pycatch22"
-    paras = CanonicalIntervalForestRegressor.get_test_params(
+    paras = CanonicalIntervalForestRegressor._get_test_params(
         parameter_set="contracting"
     )
     assert paras["time_limit_in_minutes"] == 5
diff --git a/aeon/regression/interval_based/tests/test_dr_cif.py b/aeon/regression/interval_based/tests/test_dr_cif.py
index 145c172e..d6cf83d3 100644
--- a/aeon/regression/interval_based/tests/test_dr_cif.py
+++ b/aeon/regression/interval_based/tests/test_dr_cif.py
@@ -8,6 +8,6 @@ def test_dr_cif():
     dr = DrCIFRegressor(use_pycatch22=True)
     d = dr.get_tag("python_dependencies")
     assert d[0] == "pycatch22"
-    paras = DrCIFRegressor.get_test_params(parameter_set="contracting")
+    paras = DrCIFRegressor._get_test_params(parameter_set="contracting")
     assert paras["time_limit_in_minutes"] == 5
     assert paras["att_subsample_size"] == 2
diff --git a/aeon/regression/interval_based/tests/test_interval_forest.py b/aeon/regression/interval_based/tests/test_interval_forest.py
index 55651660..173fe7df 100644
--- a/aeon/regression/interval_based/tests/test_interval_forest.py
+++ b/aeon/regression/interval_based/tests/test_interval_forest.py
@@ -5,6 +5,6 @@ from aeon.regression.interval_based import IntervalForestRegressor
 
 def test_cif():
     """Test with IntervalForestRegressor contracting."""
-    paras = IntervalForestRegressor.get_test_params(parameter_set="contracting")
+    paras = IntervalForestRegressor._get_test_params(parameter_set="contracting")
     assert paras["time_limit_in_minutes"] == 5
     assert paras["n_intervals"] == 2
diff --git a/aeon/testing/estimator_checking/_estimator_checking.py b/aeon/testing/estimator_checking/_estimator_checking.py
index 4bca59e2..a6ba9dc1 100644
--- a/aeon/testing/estimator_checking/_estimator_checking.py
+++ b/aeon/testing/estimator_checking/_estimator_checking.py
@@ -43,9 +43,9 @@ def parametrize_with_checks(
     ----------
     estimators : list of aeon BaseAeonEstimator instances or classes
         Estimators to generate checks for. If an item is a class, an instance will
-        be created using BaseAeonEstimator.create_test_instance().
+        be created using BaseAeonEstimator._create_test_instance().
     use_first_parameter_set : bool, default=False
-        If True, only the first parameter set from get_test_params will be used if a
+        If True, only the first parameter set from _get_test_params will be used if a
         class is passed.
 
     Returns
@@ -117,13 +117,13 @@ def check_estimator(
     ----------
     estimator : aeon BaseAeonEstimator instance or class
         Estimator to run checks on. If estimator is a class, an instance will
-        be created using BaseAeonEstimator.create_test_instance().
+        be created using BaseAeonEstimator._create_test_instance().
     raise_exceptions : bool, optional, default=False
         Whether to return exceptions/failures in the results dict, or raise them
             if False: returns exceptions in returned `results` dict
             if True: raises exceptions as they occur
     use_first_parameter_set : bool, default=False
-        If True, only the first parameter set from get_test_params will be used if a
+        If True, only the first parameter set from _get_test_params will be used if a
         class is passed.
     checks_to_run : str or list of str, default=None
         Name(s) of checks to run. This should include the function name of the check to
diff --git a/aeon/testing/estimator_checking/_yield_classification_checks.py b/aeon/testing/estimator_checking/_yield_classification_checks.py
index ba8ceed8..1a019b3d 100644
--- a/aeon/testing/estimator_checking/_yield_classification_checks.py
+++ b/aeon/testing/estimator_checking/_yield_classification_checks.py
@@ -100,7 +100,7 @@ def check_classifier_against_expected_results(estimator_class):
             continue
 
         # we only use the first estimator instance for testing
-        estimator_instance = estimator_class.create_test_instance(
+        estimator_instance = estimator_class._create_test_instance(
             parameter_set="results_comparison"
         )
         # set random seed if possible
@@ -141,7 +141,7 @@ def check_classifier_tags_consistent(estimator_class):
     if multivariate:
         X = np.random.random((10, 2, 20))
         y = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1])
-        inst = estimator_class.create_test_instance(parameter_set="default")
+        inst = estimator_class._create_test_instance(parameter_set="default")
         inst.fit(X, y)
         inst.predict(X)
         inst.predict_proba(X)
@@ -166,7 +166,7 @@ def check_classifier_does_not_override_final_methods(estimator_class):
 
 def check_contracted_classifier(estimator_class, datatype):
     """Test classifiers that can be contracted."""
-    estimator_instance = estimator_class.create_test_instance(
+    estimator_instance = estimator_class._create_test_instance(
         parameter_set="contracting"
     )
 
diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py
index 6f74139f..4843f130 100644
--- a/aeon/testing/estimator_checking/_yield_clustering_checks.py
+++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py
@@ -43,7 +43,7 @@ def check_clusterer_tags_consistent(estimator_class):
     multivariate = estimator_class.get_class_tag("capability:multivariate")
     if multivariate:
         X = np.random.random((10, 2, 10))
-        inst = estimator_class.create_test_instance(parameter_set="default")
+        inst = estimator_class._create_test_instance(parameter_set="default")
         inst.fit(X)
         inst.predict(X)
         inst.predict_proba(X)
diff --git a/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py b/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
index ee7636d5..c62b9d54 100644
--- a/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_collection_transformation_checks.py
@@ -35,7 +35,7 @@ def check_channel_selectors(estimator_class):
     """
     X, _ = make_example_3d_numpy(n_cases=20, n_channels=6, n_timepoints=30)
     y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
-    cs = estimator_class.create_test_instance(return_first=True)
+    cs = estimator_class._create_test_instance(return_first=True)
     assert not cs.get_tag("fit_is_empty")
     cs.fit(X, y)
     assert cs.channels_selected_ is not None
diff --git a/aeon/testing/estimator_checking/_yield_early_classification_checks.py b/aeon/testing/estimator_checking/_yield_early_classification_checks.py
index 1b071ee6..9459b394 100644
--- a/aeon/testing/estimator_checking/_yield_early_classification_checks.py
+++ b/aeon/testing/estimator_checking/_yield_early_classification_checks.py
@@ -56,7 +56,7 @@ def check_early_classifier_against_expected_results(estimator_class):
             continue
 
         # we only use the first estimator instance for testing
-        estimator_instance = estimator_class.create_test_instance(
+        estimator_instance = estimator_class._create_test_instance(
             parameter_set="results_comparison"
         )
         # set random seed if possible
diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py
index 34370855..20664bea 100644
--- a/aeon/testing/estimator_checking/_yield_estimator_checks.py
+++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py
@@ -91,7 +91,7 @@ def _yield_all_aeon_checks(
     if has_dependencies:
         if isclass(estimator) and issubclass(estimator, BaseAeonEstimator):
             estimator_class = estimator
-            estimator_instances = estimator.create_test_instance(
+            estimator_instances = estimator._create_test_instance(
                 return_first=use_first_parameter_set
             )
         elif isinstance(estimator, BaseAeonEstimator):
@@ -230,24 +230,23 @@ def _yield_estimator_checks(estimator_class, estimator_instances, datatypes):
 
 
 def check_create_test_instance(estimator_class):
-    """Check create_test_instance logic and basic constructor functionality.
+    """Check _create_test_instance logic and basic constructor functionality.
 
-    create_test_instance and create_test_instances_and_names are the
-    key methods used to create test instances in testing.
-    If this test does not pass, validity of the other tests cannot be guaranteed.
+    _create_test_instance is the key method used to create test instances in testing.
+    If this test does not pass, the validity of the other tests cannot be guaranteed.
 
     Also tests inheritance and super call logic in the constructor.
 
     Tests that:
-    * create_test_instance results in an instance of estimator_class
+    * _create_test_instance results in an instance of estimator_class
     * __init__ calls super.__init__
     * _tags_dynamic attribute for tag inspection is present after construction
     """
-    estimator = estimator_class.create_test_instance()
+    estimator = estimator_class._create_test_instance()
 
     # Check that method does not construct object of other class than itself
     assert isinstance(estimator, estimator_class), (
-        "object returned by create_test_instance must be an instance of the class, "
+        "object returned by _create_test_instance must be an instance of the class, "
         f"found {type(estimator)}"
     )
 
@@ -311,8 +310,8 @@ def check_set_params_sklearn(estimator_class):
     we use the other test parameter settings (which are assumed valid).
     This guarantees settings which play along with the __init__ content.
     """
-    estimator = estimator_class.create_test_instance()
-    test_params = estimator_class.get_test_params()
+    estimator = estimator_class._create_test_instance()
+    test_params = estimator_class._get_test_params()
     if not isinstance(test_params, list):
         test_params = [test_params]
 
@@ -342,8 +341,8 @@ def check_constructor(estimator_class):
     """Check that the constructor has sklearn compatible signature and behaviour.
 
     Based on sklearn check_estimator testing of __init__ logic.
-    Uses create_test_instance to create an instance.
-    Assumes test_create_test_instance has passed and certified create_test_instance.
+    Uses _create_test_instance to create an instance.
+    Assumes test_create_test_instance has passed and certified _create_test_instance.
 
     Tests that:
     * constructor has no varargs
@@ -358,7 +357,7 @@ def check_constructor(estimator_class):
     msg = "constructor __init__ should have no varargs"
     assert getfullargspec(estimator_class.__init__).varkw is None, msg
 
-    estimator = estimator_class.create_test_instance()
+    estimator = estimator_class._create_test_instance()
     assert isinstance(estimator, estimator_class)
 
     # Ensure that each parameter is set in init
@@ -382,7 +381,7 @@ def check_constructor(estimator_class):
 
     params = estimator.get_params()
 
-    test_params = estimator_class.get_test_params()
+    test_params = estimator_class._get_test_params()
     if isinstance(test_params, list):
         test_params = test_params[0]
     test_params = test_params.keys()
@@ -392,7 +391,7 @@ def check_constructor(estimator_class):
     for param in init_params:
         assert param.default != param.empty, (
             "parameter `%s` for %s has no default value and is not "
-            "set in `get_test_params`" % (param.name, estimator.__class__.__name__)
+            "set in _get_test_params" % (param.name, estimator.__class__.__name__)
         )
         if type(param.default) is type:
             assert param.default in [np.float64, np.int64]
diff --git a/aeon/testing/estimator_checking/_yield_regression_checks.py b/aeon/testing/estimator_checking/_yield_regression_checks.py
index 0c62c0f6..3a8e5388 100644
--- a/aeon/testing/estimator_checking/_yield_regression_checks.py
+++ b/aeon/testing/estimator_checking/_yield_regression_checks.py
@@ -71,7 +71,7 @@ def check_regressor_against_expected_results(estimator_class):
             continue
 
         # we only use the first estimator instance for testing
-        estimator_instance = estimator_class.create_test_instance(
+        estimator_instance = estimator_class._create_test_instance(
             parameter_set="results_comparison"
         )
         # set random seed if possible
@@ -115,7 +115,7 @@ def check_regressor_tags_consistent(estimator_class):
     if multivariate:
         X = np.random.random((10, 2, 20))
         y = np.random.random(10)
-        inst = estimator_class.create_test_instance(parameter_set="default")
+        inst = estimator_class._create_test_instance(parameter_set="default")
         inst.fit(X, y)
         inst.predict(X)
 
diff --git a/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py b/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
index 4539fafd..83c6f96b 100644
--- a/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
+++ b/aeon/testing/estimator_checking/_yield_soft_dependency_checks.py
@@ -30,12 +30,12 @@ def check_python_version_softdep(estimator_class):
 
     # should be compatible with python version and able to construct
     if _check_python_version(estimator_class, severity="none"):
-        estimator_class.create_test_instance()
+        estimator_class._create_test_instance()
     # should raise a specific error if python version is incompatible
     else:
         pyspec = estimator_class.get_class_tag("python_version", None)
         with pytest.raises(ModuleNotFoundError) as ex_info:
-            estimator_class.create_test_instance()
+            estimator_class._create_test_instance()
         assert "requires python version to be" in str(ex_info.value), (
             f"Estimator {estimator_class.__name__} has python version bound "
             f"{pyspec} according to tags, but does not raise an appropriate "
@@ -54,11 +54,11 @@ def check_python_dependency_softdep(estimator_class):
 
     # should be compatible with installed dependencies and able to construct
     if softdeps is None or _check_soft_dependencies(softdeps, severity="none"):
-        estimator_class.create_test_instance()
+        estimator_class._create_test_instance()
     # should raise a specific error if any soft dependencies are missing
     else:
         with pytest.raises(ModuleNotFoundError) as ex_info:
-            estimator_class.create_test_instance()
+            estimator_class._create_test_instance()
         assert (
             "is a soft dependency and not included in the base aeon installation"
             in str(ex_info.value)
diff --git a/aeon/testing/estimator_checking/_yield_transformation_checks.py b/aeon/testing/estimator_checking/_yield_transformation_checks.py
index 6bb5f0d9..6383c879 100644
--- a/aeon/testing/estimator_checking/_yield_transformation_checks.py
+++ b/aeon/testing/estimator_checking/_yield_transformation_checks.py
@@ -59,7 +59,7 @@ def check_transformer_against_expected_results(estimator_class):
             continue
 
         # we only use the first estimator instance for testing
-        estimator_instance = estimator_class.create_test_instance(
+        estimator_instance = estimator_class._create_test_instance(
             parameter_set="results_comparison"
         )
         # set random seed if possible
diff --git a/aeon/testing/estimator_checking/tests/test_check_estimator.py b/aeon/testing/estimator_checking/tests/test_check_estimator.py
index 1b2faa0e..f8f5b41f 100644
--- a/aeon/testing/estimator_checking/tests/test_check_estimator.py
+++ b/aeon/testing/estimator_checking/tests/test_check_estimator.py
@@ -44,7 +44,7 @@ def test_parametrize_with_checks_classes(check):
     assert equal, msg
 
 
-test_instances = [c.create_test_instance() for c in list(test_classes.values())]
+test_instances = [c._create_test_instance() for c in list(test_classes.values())]
 test_instances = {c.__class__.__name__: c for c in test_instances}
 
 
@@ -63,7 +63,7 @@ def test_parametrize_with_checks_instances(check):
 @pytest.mark.parametrize("estimator_class", list(test_classes.values()))
 def test_check_estimator_passed(estimator_class):
     """Test that check_estimator returns only passed tests for examples we know pass."""
-    estimator = estimator_class.create_test_instance()
+    estimator = estimator_class._create_test_instance()
 
     result_class = check_estimator(estimator_class, verbose=False)
     assert all(x == "PASSED" for x in result_class.values())
diff --git a/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
index 441a4964..aff13ceb 100644
--- a/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/classifier_results_reproduction.py
@@ -116,153 +116,159 @@ def _print_array(test_name, array):
 
 def _print_results_for_classifier(classifier_name, dataset_name):
     if classifier_name == "ChannelEnsembleClassifier":
-        classifier = ChannelEnsembleClassifier.create_test_instance(
+        classifier = ChannelEnsembleClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "WeightedEnsembleClassifier":
-        classifier = WeightedEnsembleClassifier.create_test_instance(
+        classifier = WeightedEnsembleClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "ClassifierPipeline":
-        classifier = ClassifierPipeline.create_test_instance(
+        classifier = ClassifierPipeline._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "BOSSEnsemble":
-        classifier = BOSSEnsemble.create_test_instance(
+        classifier = BOSSEnsemble._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "ContractableBOSS":
-        classifier = ContractableBOSS.create_test_instance(
+        classifier = ContractableBOSS._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "MUSE":
-        classifier = MUSE.create_test_instance(parameter_set="results_comparison")
+        classifier = MUSE._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "TemporalDictionaryEnsemble":
-        classifier = TemporalDictionaryEnsemble.create_test_instance(
+        classifier = TemporalDictionaryEnsemble._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "WEASEL":
-        classifier = WEASEL.create_test_instance(parameter_set="results_comparison")
+        classifier = WEASEL._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "WEASEL_V2":
-        classifier = WEASEL_V2.create_test_instance(parameter_set="results_comparison")
+        classifier = WEASEL_V2._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "REDCOMETS":
-        classifier = REDCOMETS.create_test_instance(parameter_set="results_comparison")
+        classifier = REDCOMETS._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "ElasticEnsemble":
-        classifier = ElasticEnsemble.create_test_instance(
+        classifier = ElasticEnsemble._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "KNeighborsTimeSeriesClassifier":
-        classifier = KNeighborsTimeSeriesClassifier.create_test_instance(
+        classifier = KNeighborsTimeSeriesClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "Catch22Classifier":
-        classifier = Catch22Classifier.create_test_instance(
+        classifier = Catch22Classifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "FreshPRINCEClassifier":
-        classifier = FreshPRINCEClassifier.create_test_instance(
+        classifier = FreshPRINCEClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "RandomIntervalClassifier":
-        classifier = RandomIntervalClassifier.create_test_instance(
+        classifier = RandomIntervalClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "QUANTClassifier":
-        classifier = QUANTClassifier.create_test_instance(
+        classifier = QUANTClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "SignatureClassifier":
-        classifier = SignatureClassifier.create_test_instance(
+        classifier = SignatureClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "SummaryClassifier":
-        classifier = SummaryClassifier.create_test_instance(
+        classifier = SummaryClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "TSFreshClassifier":
-        classifier = TSFreshClassifier.create_test_instance(
+        classifier = TSFreshClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "HIVECOTEV1":
-        classifier = HIVECOTEV1.create_test_instance(parameter_set="results_comparison")
+        classifier = HIVECOTEV1._create_test_instance(
+            parameter_set="results_comparison"
+        )
     elif classifier_name == "HIVECOTEV2":
-        classifier = HIVECOTEV2.create_test_instance(parameter_set="results_comparison")
+        classifier = HIVECOTEV2._create_test_instance(
+            parameter_set="results_comparison"
+        )
     elif classifier_name == "CanonicalIntervalForestClassifier":
-        classifier = CanonicalIntervalForestClassifier.create_test_instance(
+        classifier = CanonicalIntervalForestClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "DrCIFClassifier":
-        classifier = DrCIFClassifier.create_test_instance(
+        classifier = DrCIFClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "IntervalForestClassifier":
-        classifier = IntervalForestClassifier.create_test_instance(
+        classifier = IntervalForestClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "RandomIntervalSpectralEnsembleClassifier":
-        classifier = RandomIntervalSpectralEnsembleClassifier.create_test_instance(
+        classifier = RandomIntervalSpectralEnsembleClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "RSTSF":
-        classifier = RSTSF.create_test_instance(parameter_set="results_comparison")
+        classifier = RSTSF._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "SupervisedTimeSeriesForest":
-        classifier = SupervisedTimeSeriesForest.create_test_instance(
+        classifier = SupervisedTimeSeriesForest._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "TimeSeriesForestClassifier":
-        classifier = TimeSeriesForestClassifier.create_test_instance(
+        classifier = TimeSeriesForestClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "Arsenal":
-        classifier = Arsenal.create_test_instance(parameter_set="results_comparison")
+        classifier = Arsenal._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "RocketClassifier":
-        classifier = RocketClassifier.create_test_instance(
+        classifier = RocketClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "HydraClassifier":
-        classifier = HydraClassifier.create_test_instance(
+        classifier = HydraClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "MultiRocketHydraClassifier":
-        classifier = MultiRocketHydraClassifier.create_test_instance(
+        classifier = MultiRocketHydraClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "OrdinalTDE":
-        classifier = OrdinalTDE.create_test_instance(parameter_set="results_comparison")
+        classifier = OrdinalTDE._create_test_instance(
+            parameter_set="results_comparison"
+        )
     elif classifier_name == "ShapeletTransformClassifier":
-        classifier = ShapeletTransformClassifier.create_test_instance(
+        classifier = ShapeletTransformClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "LearningShapeletClassifier":
-        classifier = LearningShapeletClassifier.create_test_instance(
+        classifier = LearningShapeletClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "MrSQMClassifier":
-        classifier = MrSQMClassifier.create_test_instance(
+        classifier = MrSQMClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "SASTClassifier":
-        classifier = SASTClassifier.create_test_instance(
+        classifier = SASTClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "ContinuousIntervalTree":
-        classifier = ContinuousIntervalTree.create_test_instance(
+        classifier = ContinuousIntervalTree._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "RotationForestClassifier":
-        classifier = RotationForestClassifier.create_test_instance(
+        classifier = RotationForestClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "ProbabilityThresholdEarlyClassifier":
-        classifier = ProbabilityThresholdEarlyClassifier.create_test_instance(
+        classifier = ProbabilityThresholdEarlyClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "BaseEarlyClassifier":
-        classifier = BaseEarlyClassifier.create_test_instance(
+        classifier = BaseEarlyClassifier._create_test_instance(
             parameter_set="results_comparison"
         )
     elif classifier_name == "TEASER":
-        classifier = TEASER.create_test_instance(parameter_set="results_comparison")
+        classifier = TEASER._create_test_instance(parameter_set="results_comparison")
     elif classifier_name == "TEASER-IF":
         classifier = TEASER(
             classification_points=[6, 10, 16, 24],
diff --git a/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
index 5c47340e..9d300787 100644
--- a/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/regressor_results_reproduction.py
@@ -60,67 +60,67 @@ def _print_array(test_name, array):
 
 def _print_results_for_regressor(regressor_name, dataset_name):
     if regressor_name == "FreshPRINCERegressor":
-        regressor = FreshPRINCERegressor.create_test_instance(
+        regressor = FreshPRINCERegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "Catch22Regressor":
-        regressor = Catch22Regressor.create_test_instance(
+        regressor = Catch22Regressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "SummaryRegressor":
-        regressor = SummaryRegressor.create_test_instance(
+        regressor = SummaryRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "TSFreshRegressor":
-        regressor = TSFreshRegressor.create_test_instance(
+        regressor = TSFreshRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "HydraRegressor":
-        regressor = HydraRegressor.create_test_instance(
+        regressor = HydraRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "MultiRocketHydraRegressor":
-        regressor = MultiRocketHydraRegressor.create_test_instance(
+        regressor = MultiRocketHydraRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "RocketRegressor":
-        regressor = RocketRegressor.create_test_instance(
+        regressor = RocketRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "KNeighborsTimeSeriesRegressor":
-        regressor = KNeighborsTimeSeriesRegressor.create_test_instance(
+        regressor = KNeighborsTimeSeriesRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "RISTRegressor":
-        regressor = RISTRegressor.create_test_instance(
+        regressor = RISTRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "CanonicalIntervalForestRegressor":
-        regressor = CanonicalIntervalForestRegressor.create_test_instance(
+        regressor = CanonicalIntervalForestRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "DrCIFRegressor":
-        regressor = DrCIFRegressor.create_test_instance(
+        regressor = DrCIFRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "IntervalForestRegressor":
-        regressor = IntervalForestRegressor.create_test_instance(
+        regressor = IntervalForestRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "RandomIntervalRegressor":
-        regressor = RandomIntervalRegressor.create_test_instance(
+        regressor = RandomIntervalRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "RandomIntervalSpectralEnsembleRegressor":
-        regressor = RandomIntervalSpectralEnsembleRegressor.create_test_instance(
+        regressor = RandomIntervalSpectralEnsembleRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "TimeSeriesForestRegressor":
-        regressor = TimeSeriesForestRegressor.create_test_instance(
+        regressor = TimeSeriesForestRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     elif regressor_name == "RDSTRegressor":
-        regressor = RDSTRegressor.create_test_instance(
+        regressor = RDSTRegressor._create_test_instance(
             parameter_set="results_comparison"
         )
     else:
diff --git a/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py b/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
index 75d79f20..3b8e8809 100644
--- a/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
+++ b/aeon/testing/expected_results/results_reproduction/transform_results_reproduction.py
@@ -45,15 +45,15 @@ def _print_array(test_name, array):
 
 def _print_results_for_transformer(transformer_name, dataset_name):
     if transformer_name == "RandomIntervals":
-        transformer = RandomIntervals.create_test_instance(
+        transformer = RandomIntervals._create_test_instance(
             parameter_set="results_comparison"
         )
     elif transformer_name == "SupervisedIntervals":
-        transformer = SupervisedIntervals.create_test_instance(
+        transformer = SupervisedIntervals._create_test_instance(
             parameter_set="results_comparison"
         )
     elif transformer_name == "RandomShapeletTransform":
-        transformer = RandomShapeletTransform.create_test_instance(
+        transformer = RandomShapeletTransform._create_test_instance(
             parameter_set="results_comparison"
         )
     else:
diff --git a/aeon/testing/mock_estimators/_mock_classifiers.py b/aeon/testing/mock_estimators/_mock_classifiers.py
index 6b4acd3c..af18857a 100644
--- a/aeon/testing/mock_estimators/_mock_classifiers.py
+++ b/aeon/testing/mock_estimators/_mock_classifiers.py
@@ -63,7 +63,7 @@ class MockClassifierMultiTestParams(BaseClassifier):
         return np.zeros(shape=(len(X),))
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
 
         Parameters
@@ -78,6 +78,5 @@ class MockClassifierMultiTestParams(BaseClassifier):
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
             `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
         """
         return [{"return_ones": False}, {"return_ones": True}]
diff --git a/aeon/testing/mock_estimators/_mock_segmenters.py b/aeon/testing/mock_estimators/_mock_segmenters.py
index e8005a1c..82ff6a81 100644
--- a/aeon/testing/mock_estimators/_mock_segmenters.py
+++ b/aeon/testing/mock_estimators/_mock_segmenters.py
@@ -26,7 +26,7 @@ class MockSegmenter(BaseSegmenter):
         return np.array([1])
 
     @classmethod
-    def get_test_params(cls, parameter_set="default"):
+    def _get_test_params(cls, parameter_set="default"):
         """
         Return testing parameter settings for the estimator.
 
diff --git a/aeon/visualisation/estimator/tests/test_shapelet_plotting.py b/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
index c4f01f03..5b2708cb 100644
--- a/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
+++ b/aeon/visualisation/estimator/tests/test_shapelet_plotting.py
@@ -97,7 +97,9 @@ def test_ShapeletTransformerVisualizer(transformer_class):
     import matplotlib.pyplot as plt
 
     X, y = make_example_3d_numpy()
-    shp_transformer = transformer_class(**transformer_class.get_test_params()).fit(X, y)
+    shp_transformer = transformer_class(**transformer_class._get_test_params()).fit(
+        X, y
+    )
     shp_vis = ShapeletTransformerVisualizer(shp_transformer)
 
     fig = shp_vis.plot(0)
@@ -126,7 +128,7 @@ def test_ShapeletClassifierVisualizer(classifier_class):
     import matplotlib.pyplot as plt
 
     X, y = make_example_3d_numpy()
-    shp_transformer = classifier_class(**classifier_class.get_test_params()).fit(X, y)
+    shp_transformer = classifier_class(**classifier_class._get_test_params()).fit(X, y)
     shp_vis = ShapeletClassifierVisualizer(shp_transformer)
 
     fig = shp_vis.plot(0)