diff --git a/optgbm/sklearn.py b/optgbm/sklearn.py
index 6e9c852..4052c06 100644
--- a/optgbm/sklearn.py
+++ b/optgbm/sklearn.py
@@ -152,10 +152,17 @@ def __call__(self, trial: trial_module.Trial) -> float:
             num_boost_round=self.n_estimators,
         )  # Dict[str, List[float]]
         best_iteration = callbacks[0].best_iteration_  # type: ignore
+        values = eval_hist[
+            "{}-mean".format(self.eval_name)
+        ]  # type: List[float]
+        evals_result = {
+            "cv_agg": {self.eval_name: values}
+        }  # type: Dict[str, Dict[str, List[float]]]
 
         trial.set_user_attr("best_iteration", best_iteration)
+        trial.set_user_attr("evals_result", evals_result)
 
-        value = eval_hist["{}-mean".format(self.eval_name)][-1]  # type: float
+        value = values[-1]  # type: float
         is_best_trial = True  # type: bool
 
         try:
@@ -650,6 +657,7 @@ def fit(
             None if early_stopping_rounds is None else best_iteration
         )
         self._best_score = self.study_.best_value
+        self._evals_result = self.study_.best_trial.user_attrs["evals_result"]
         self._objective = params["objective"]
         self.best_params_ = {**params, **self.study_.best_params}
         self.n_splits_ = cv.get_n_splits(X, y, groups=groups)
diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py
index 22fe1c6..1b5ea56 100644
--- a/tests/test_sklearn.py
+++ b/tests/test_sklearn.py
@@ -374,3 +374,15 @@ def test_plot_importance(n_jobs: int) -> None:
     clf.fit(X, y)
 
     lgb.plot_importance(clf)
+
+
+def test_plot_metric() -> None:
+    X, y = load_breast_cancer(return_X_y=True)
+
+    clf = OGBMClassifier(
+        n_estimators=n_estimators, n_trials=n_trials, refit=False
+    )
+
+    clf.fit(X, y)
+
+    lgb.plot_metric(clf)