diff --git a/skpro/base/old_base.py b/skpro/base/old_base.py
index f20fe3ecc..0fd467079 100644
--- a/skpro/base/old_base.py
+++ b/skpro/base/old_base.py
@@ -179,7 +179,7 @@ def _cached(f):
     Decorated function
     """
 
-    @functools.lru_cache()
+    @functools.lru_cache
     def wrapper(self, *args, **kwargs):
         return f(self, *args, **kwargs)
 
@@ -449,8 +449,7 @@ def ppf(self, q, *args, **kwargs):
             )
 
         def lp2(self):
-            """
-            Implements the Lp2 norm of the probability density function
+            r"""Implements the Lp2 norm of the probability density function.
 
             ..math::
             L^2 = \int PDF(x)^2 dx
@@ -698,7 +697,7 @@ class BayesianVendorInterface(VendorInterface):
     """
 
     @abc.abstractmethod
-    @functools.lru_cache()
+    @functools.lru_cache
     def samples(self):
         """
         Returns the predictive posterior samples
diff --git a/skpro/tests/test_baselines.py b/skpro/tests/test_baselines.py
index 27c49f1e8..f37a67470 100644
--- a/skpro/tests/test_baselines.py
+++ b/skpro/tests/test_baselines.py
@@ -20,8 +20,8 @@ def test_density_baseline():
     # median prediction working?
     mu = np.mean(data.y_train)
     sigma = np.std(data.y_train)
-    assert (y_pred.point() == np.ones((len(data.X_test))) * mu).all()
-    assert (y_pred.std() == np.ones((len(data.X_test))) * sigma).all()
+    assert (y_pred.point() == np.ones(len(data.X_test)) * mu).all()
+    assert (y_pred.std() == np.ones(len(data.X_test)) * sigma).all()
 
     # pdf, cdf working?
     x = np.random.randint(0, 10)
diff --git a/skpro/tests/test_ensemble.py b/skpro/tests/test_ensemble.py
index 45fcdf96e..5a17b9712 100644
--- a/skpro/tests/test_ensemble.py
+++ b/skpro/tests/test_ensemble.py
@@ -1,14 +1,10 @@
-# LEGACY MODULE - TODO: remove or refactor
+"""LEGACY MODULE - TODO: remove or refactor."""
 
 import pytest
 from sklearn.ensemble import BaggingRegressor as ClassicBaggingRegressor
-from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error as mse
-from sklearn.svm import SVR
 from sklearn.tree import DecisionTreeRegressor
 
-from skpro.regression.ensemble import BaggingRegressor as SkproBaggingRegressor
-from skpro.regression.parametric.parametric import ParametricEstimator
 from skpro.workflow.manager import DataManager