diff --git a/skore/src/skore/sklearn/_estimator.py b/skore/src/skore/sklearn/_estimator.py
index 02492fb82..57772a341 100644
--- a/skore/src/skore/sklearn/_estimator.py
+++ b/skore/src/skore/sklearn/_estimator.py
@@ -892,7 +892,7 @@ def report_metrics(
                     metrics_kwargs["pos_label"] = pos_label
             else:
                 raise ValueError(
-                    f"Invalid type of metric: {type(metric)} for metric: {metric}"
+                    f"Invalid type of metric: {type(metric)} for {metric!r}"
                 )
 
             scores.append(
diff --git a/skore/tests/unit/sklearn/test_estimator.py b/skore/tests/unit/sklearn/test_estimator.py
index 06ed25796..7086376b2 100644
--- a/skore/tests/unit/sklearn/test_estimator.py
+++ b/skore/tests/unit/sklearn/test_estimator.py
@@ -778,6 +778,16 @@ def custom_metric(y_true, y_pred, some_weights):
     )
 
 
+def test_estimator_report_report_metrics_invalid_metric_type(regression_data):
+    """Check that we raise the expected error message if an invalid metric is passed."""
+    estimator, X_test, y_test = regression_data
+    report = EstimatorReport(estimator, X_test=X_test, y_test=y_test)
+
+    err_msg = re.escape("Invalid type of metric: <class 'int'> for 1")
+    with pytest.raises(ValueError, match=err_msg):
+        report.metrics.report_metrics(scoring=[1])
+
+
 def test_estimator_report_get_X_y_and_data_source_hash_error():
     """Check that we raise the proper error in `get_X_y_and_use_cache`."""
     X, y = make_classification(n_classes=2, random_state=42)