diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index b00cb7762..2703b8160 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -334,6 +334,10 @@ class XGBModel(XGBModelBase):
             else:
                 self.kwargs[key] = value
 
+        if hasattr(self, '_Booster'):
+            parameters = self.get_xgb_params()
+            self.get_booster().set_param(parameters)
+
         return self
 
     def get_params(self, deep=True):
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index 2ffadb188..318c349f3 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -542,13 +542,29 @@ def test_sklearn_n_jobs():
     assert clf.get_xgb_params()['n_jobs'] == 2
 
 
-def test_kwargs():
+def test_parameters_access():
+    from sklearn import datasets
     params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
     clf = xgb.XGBClassifier(n_estimators=1000, **params)
     assert clf.get_params()['updater'] == 'grow_gpu_hist'
     assert clf.get_params()['subsample'] == .5
     assert clf.get_params()['n_estimators'] == 1000
 
+    clf = xgb.XGBClassifier(n_estimators=1, nthread=4)
+    X, y = datasets.load_iris(return_X_y=True)
+    clf.fit(X, y)
+
+    config = json.loads(clf.get_booster().save_config())
+    assert int(config['learner']['generic_param']['nthread']) == 4
+
+    clf.set_params(nthread=16)
+    config = json.loads(clf.get_booster().save_config())
+    assert int(config['learner']['generic_param']['nthread']) == 16
+
+    clf.predict(X)
+    config = json.loads(clf.get_booster().save_config())
+    assert int(config['learner']['generic_param']['nthread']) == 16
+
 
 def test_kwargs_error():
     params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}