diff --git a/.github/workflows/pythonapp.yml b/.github/workflows/pythonapp.yml
index 6708aa97..d8b9824b 100644
--- a/.github/workflows/pythonapp.yml
+++ b/.github/workflows/pythonapp.yml
@@ -144,7 +144,7 @@ jobs:
     # We don't run pytest for Linux py3.8 since we do coverage for that case.
     - name: Test with pytest
       if: ${{ matrix.python-version != '3.8' || startsWith(matrix.os, 'ubuntu') != true }}
-      run: pytest -k test_extra_trees_tvm_tree_trav_regressor_converter -s
+      run: pytest -v
     # Run and push coverage only for Linux py3.8
     - name: Coverage 3.8 Linux
       if: ${{ matrix.python-version == '3.8' && startsWith(matrix.os, 'ubuntu') }}
diff --git a/tests/test_sklearn_decision_tree_converter.py b/tests/test_sklearn_decision_tree_converter.py
index 14477a97..abfa5d69 100644
--- a/tests/test_sklearn_decision_tree_converter.py
+++ b/tests/test_sklearn_decision_tree_converter.py
@@ -130,16 +130,13 @@ def test_random_forest_perf_tree_trav_classifier_shifted_labels_converter(self):
     def _run_tree_regressor_converter(self, model_type, num_classes, backend="torch", extra_config={}, **kwargs):
         warnings.filterwarnings("ignore")
         for max_depth in [1, 3, 8, 10, 12, None]:
-            print(f"max_depth: {max_depth}")
             model = model_type(max_depth=max_depth, **kwargs)
             np.random.seed(0)
             X = np.random.rand(100, 200)
             X = np.array(X, dtype=np.float32)
             y = np.random.randint(num_classes, size=100)
 
-            print("  start training on skl")
             model.fit(X, y)
-            print("  convert skl model with HB")
             torch_model = hummingbird.ml.convert(model, backend, X, extra_config=extra_config)
             self.assertIsNotNone(torch_model)
             np.testing.assert_allclose(model.predict(X), torch_model.predict(X), rtol=1e-06, atol=1e-06)