diff --git a/optuna_integration/pytorch_lightning/pytorch_lightning.py b/optuna_integration/pytorch_lightning/pytorch_lightning.py
index b2cf3a92..c2b68772 100644
--- a/optuna_integration/pytorch_lightning/pytorch_lightning.py
+++ b/optuna_integration/pytorch_lightning/pytorch_lightning.py
@@ -52,7 +52,7 @@ class PyTorchLightningPruningCallback(Callback):
 
     .. note::
         If you would like to use PyTorchLightningPruningCallback in a distributed training
-        environment, you need to evoke `PyTorchLightningPruningCallback.check_pruned()`
+        environment, you need to evoke ``PyTorchLightningPruningCallback.check_pruned()``
         manually so that :class:`~optuna.exceptions.TrialPruned` is properly handled.
     """
 
@@ -148,7 +148,7 @@ def check_pruned(self) -> None:
         """Raise :class:`optuna.TrialPruned` manually if pruned.
 
         Currently, ``intermediate_values`` are not properly propagated between processes due to
-        storage cache. Therefore, necessary information is kept in trial_system_attrs when the
+        storage cache. Therefore, necessary information is kept in ``trial.system_attrs`` when the
         trial runs in a distributed situation. Please call this method right after calling
         ``lightning.pytorch.Trainer.fit()``.
         If a callback doesn't have any backend storage for DDP, this method does nothing.
diff --git a/optuna_integration/shap/shap.py b/optuna_integration/shap/shap.py
index d20d5d13..cccbdb2c 100644
--- a/optuna_integration/shap/shap.py
+++ b/optuna_integration/shap/shap.py
@@ -34,8 +34,7 @@ class ShapleyImportanceEvaluator(BaseImportanceEvaluator):
 
         This evaluator requires the `sklearn <https://scikit-learn.org/stable/>`_ Python package
         and `SHAP <https://shap.readthedocs.io/en/stable/index.html>`_.
-        The model for the SHAP calculation is based on `sklearn.ensemble.RandomForestClassifier
-        <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_.
+        The model for the SHAP calculation is based on :class:`sklearn.ensemble.RandomForestClassifier`.
 
     Args:
         n_trees:
diff --git a/optuna_integration/sklearn/sklearn.py b/optuna_integration/sklearn/sklearn.py
index dab346ef..6818ba07 100644
--- a/optuna_integration/sklearn/sklearn.py
+++ b/optuna_integration/sklearn/sklearn.py
@@ -153,7 +153,7 @@ class _Objective:
 
         error_score:
             Value to assign to the score if an error occurs in fitting. If
-            'raise', the error is raised. If numeric,
+            ``"raise"``, the error is raised. If numeric,
             :class:`sklearn.exceptions.FitFailedWarning` is raised. This does not
             affect the refit step, which will always raise the error.
 
@@ -429,7 +429,7 @@ class OptunaSearchCV(BaseEstimator):
 
         error_score:
             Value to assign to the score if an error occurs in fitting. If
-            'raise', the error is raised. If numeric,
+            ``"raise"``, the error is raised. If numeric,
             :class:`sklearn.exceptions.FitFailedWarning` is raised. This does not
             affect the refit step, which will always raise the error.