diff --git a/skpro/tests/test_set_output.py b/skpro/tests/test_set_output.py
index ab869fb6..dd421e03 100644
--- a/skpro/tests/test_set_output.py
+++ b/skpro/tests/test_set_output.py
@@ -1,13 +1,16 @@
 import pytest
 
 from skpro.datatypes._table._convert import convert_pandas_to_polars_eager
-from skpro.tests.test_switch import run_test_module_changed
 
 # from skpro.utils.set_output import check_output_config  # SUPPORTED_OUTPUTS,
 from skpro.utils.validation._dependencies import _check_soft_dependencies
 
+# from skpro.tests.test_switch import run_test_module_changed
+
+
 if _check_soft_dependencies(["polars", "pyarrow"], severity="none"):
-    import polars as pl
+    # import polars as pl
+    pass
 
 import pandas as pd
 from sklearn.datasets import load_diabetes
@@ -89,41 +92,41 @@ def estimator():
 #     assert dense == {}
 
 
-@pytest.mark.skipif(
-    not run_test_module_changed("skpro.datatypes")
-    or not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
-    reason="skip test if polars/pyarrow is not installed in environment",
-)
-def test_set_output_pandas_polars(polars_load_diabetes_pandas, estimator):
-    X_train, X_test, y_train = polars_load_diabetes_pandas
-    estimator.fit(X_train, y_train)
-    estimator.set_output(transform="polars")
+# @pytest.mark.skipif(
+#     not run_test_module_changed("skpro.datatypes")
+#     or not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+#     reason="skip test if polars/pyarrow is not installed in environment",
+# )
+# def test_set_output_pandas_polars(polars_load_diabetes_pandas, estimator):
+#     X_train, X_test, y_train = polars_load_diabetes_pandas
+#     estimator.fit(X_train, y_train)
+#     estimator.set_output(transform="polars")
 
-    y_pred = estimator.predict(X_test)
-    assert isinstance(y_pred, pl.DataFrame)
+#     y_pred = estimator.predict(X_test)
+#     assert isinstance(y_pred, pl.DataFrame)
 
-    y_pred_interval = estimator.predict_interval(X_test)
-    assert isinstance(y_pred_interval, pl.DataFrame)
+#     y_pred_interval = estimator.predict_interval(X_test)
+#     assert isinstance(y_pred_interval, pl.DataFrame)
 
-    y_pred_quantiles = estimator.predict_quantiles(X_test)
-    assert isinstance(y_pred_quantiles, pl.DataFrame)
+#     y_pred_quantiles = estimator.predict_quantiles(X_test)
+#     assert isinstance(y_pred_quantiles, pl.DataFrame)
 
 
-@pytest.mark.skipif(
-    not run_test_module_changed("skpro.datatypes")
-    or not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
-    reason="skip test if polars/pyarrow is not installed in environment",
-)
-def test_set_output_polars_pandas(polars_load_diabetes_polars, estimator):
-    X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
-    estimator.fit(X_train_pl, y_train_pl)
-    estimator.set_output(transform="pandas")
+# @pytest.mark.skipif(
+#     not run_test_module_changed("skpro.datatypes")
+#     or not _check_soft_dependencies(["polars", "pyarrow"], severity="none"),
+#     reason="skip test if polars/pyarrow is not installed in environment",
+# )
+# def test_set_output_polars_pandas(polars_load_diabetes_polars, estimator):
+#     X_train_pl, X_test_pl, y_train_pl = polars_load_diabetes_polars
+#     estimator.fit(X_train_pl, y_train_pl)
+#     estimator.set_output(transform="pandas")
 
-    y_pred = estimator.predict(X_test_pl)
-    assert isinstance(y_pred, pd.DataFrame)
+#     y_pred = estimator.predict(X_test_pl)
+#     assert isinstance(y_pred, pd.DataFrame)
 
-    y_pred_interval = estimator.predict_interval(X_test_pl)
-    assert isinstance(y_pred_interval, pd.DataFrame)
+#     y_pred_interval = estimator.predict_interval(X_test_pl)
+#     assert isinstance(y_pred_interval, pd.DataFrame)
 
-    y_pred_quantiles = estimator.predict_quantiles(X_test_pl)
-    assert isinstance(y_pred_quantiles, pd.DataFrame)
+#     y_pred_quantiles = estimator.predict_quantiles(X_test_pl)
+#     assert isinstance(y_pred_quantiles, pd.DataFrame)