diff --git a/tests/tests_explainer/test_explainer_tabular.py b/tests/tests_explainer/test_explainer_tabular.py
index 829eb586..0d5f33bf 100644
--- a/tests/tests_explainer/test_explainer_tabular.py
+++ b/tests/tests_explainer/test_explainer_tabular.py
@@ -176,25 +176,34 @@ def test_explain(dt_model, data, index, budget, max_order, imputer):
 
 def test_against_shap_linear():
     """Tests weather TabularExplainer yields similar results as SHAP with a basic linear model."""
-    import shap
 
     n_samples = 3
     dim = 5
+    rng = np.random.default_rng(42)
 
     def make_linear_model():
-        w = np.random.default_rng().normal(size=dim)
+        w = rng.normal(size=dim)
 
         def model(X: np.ndarray):
             return np.dot(X, w)
 
         return model
 
-    X = np.random.default_rng().normal(size=(n_samples, dim))
+    X = rng.normal(size=(n_samples, dim))
     model = make_linear_model()
 
+    # import shap
     # compute with shap
-    explainer_shap = shap.explainers.Exact(model, X)
-    shap_values = explainer_shap(X).values
+    # explainer_shap = shap.explainers.Exact(model, X)
+    # shap_values = explainer_shap(X).values
+    # print(shap_values)
+    shap_values = np.array(
+        [
+            [-0.29565839, -0.36698085, -0.55970434, 0.22567077, 0.05852208],
+            [1.08513574, 0.06365536, 0.46312977, -0.61532757, 0.00370387],
+            [-0.78947735, 0.30332549, 0.09657457, 0.38965679, -0.06222595],
+        ]
+    )
 
     # compute with shapiq
     explainer_shapiq = TabularExplainer(