diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py
index c4853e10f05a..248b58643f03 100644
--- a/python-package/lightgbm/sklearn.py
+++ b/python-package/lightgbm/sklearn.py
@@ -607,6 +607,8 @@ def _get_meta_data(collection, name, i):
 
         self._best_score = self._Booster.best_score
 
+        self.fitted_ = True
+
         # free dataset
         self._Booster.free_dataset()
         del train_set, valid_sets
diff --git a/tests/python_package_test/test_sklearn.py b/tests/python_package_test/test_sklearn.py
index 47d0697b2e68..7dc39de23224 100644
--- a/tests/python_package_test/test_sklearn.py
+++ b/tests/python_package_test/test_sklearn.py
@@ -20,6 +20,7 @@
                                  RegressorChain)
 from sklearn.utils.estimator_checks import (_yield_all_checks, SkipTest,
                                             check_parameters_default_constructible)
+from sklearn.utils.validation import check_is_fitted
 
 
 decreasing_generator = itertools.count(0, -1)
@@ -1091,3 +1092,23 @@ def test_continue_training_with_model(self):
         self.assertEqual(len(init_gbm.evals_result_['valid_0']['multi_logloss']), 5)
         self.assertLess(gbm.evals_result_['valid_0']['multi_logloss'][-1],
                         init_gbm.evals_result_['valid_0']['multi_logloss'][-1])
+
+    # sklearn < 0.22 requires passing "attributes" argument
+    @unittest.skipIf(sk_version < '0.22.0', 'scikit-learn version is less than 0.22')
+    def test_check_is_fitted(self):
+        X, y = load_digits(n_class=2, return_X_y=True)
+        est = lgb.LGBMModel(n_estimators=5, objective="binary")
+        clf = lgb.LGBMClassifier(n_estimators=5)
+        reg = lgb.LGBMRegressor(n_estimators=5)
+        rnk = lgb.LGBMRanker(n_estimators=5)
+        models = (est, clf, reg, rnk)
+        for model in models:
+            self.assertRaises(lgb.compat.LGBMNotFittedError,
+                              check_is_fitted,
+                              model)
+        est.fit(X, y)
+        clf.fit(X, y)
+        reg.fit(X, y)
+        rnk.fit(X, y, group=np.ones(X.shape[0]))
+        for model in models:
+            check_is_fitted(model)