diff --git a/tests/system/small/ml/test_llm.py b/tests/system/small/ml/test_llm.py
index a82be38017..dca3c35d84 100644
--- a/tests/system/small/ml/test_llm.py
+++ b/tests/system/small/ml/test_llm.py
@@ -417,6 +417,7 @@ def test_llm_palm_score_params(llm_fine_tune_df_default_index):
     )
 
 
+@pytest.mark.flaky(retries=2)
 @pytest.mark.parametrize(
     "model_name",
     (
diff --git a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
index 479be19596..a85c6fae8d 100644
--- a/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
+++ b/third_party/bigframes_vendored/sklearn/linear_model/_logistic.py
@@ -23,6 +23,37 @@
 class LogisticRegression(LinearClassifierMixin, BaseEstimator):
     """Logistic Regression (aka logit, MaxEnt) classifier.
 
+    >>> from bigframes.ml.linear_model import LogisticRegression
+    >>> import bigframes.pandas as bpd
+    >>> bpd.options.display.progress_bar = None
+    >>> X = bpd.DataFrame({ \
+            "feature0": [20, 21, 19, 18], \
+            "feature1": [0, 1, 1, 0], \
+            "feature2": [0.2, 0.3, 0.4, 0.5]})
+    >>> y = bpd.DataFrame({"outcome": [0, 0, 1, 1]})
+    >>> # Create the LogisticRegression
+    >>> model = LogisticRegression()
+    >>> model.fit(X, y)
+    LogisticRegression()
+    >>> model.predict(X) # doctest:+SKIP
+        predicted_outcome	predicted_outcome_probs	feature0	feature1	feature2
+    0	0	[{'label': 1, 'prob': 3.1895929877221615e-07} ...	20	0	0.2
+    1	0	[{'label': 1, 'prob': 5.662891265051953e-06} ...	21	1	0.3
+    2	1	[{'label': 1, 'prob': 0.9999917826885262} {'l...	19	1	0.4
+    3	1	[{'label': 1, 'prob': 0.9999999993659574} {'l...	18	0	0.5
+    4 rows × 5 columns
+
+    [4 rows x 5 columns in total]
+
+    >>> # Score the model
+    >>> score = model.score(X, y)
+    >>> score  # doctest:+SKIP
+        precision	recall	accuracy	f1_score	log_loss	roc_auc
+    0	1.0	1.0	1.0	1.0	0.000004	1.0
+    1 rows × 6 columns
+
+    [1 rows x 6 columns in total]
+
     Args:
         optimize_strategy (str, default "auto_strategy"):
             The strategy to train logistic regression models. Possible values are