diff --git a/demo/guide-python/README.md b/demo/guide-python/README.md
index 6fd6a090c..de6e8e024 100644
--- a/demo/guide-python/README.md
+++ b/demo/guide-python/README.md
@@ -1,7 +1,6 @@
 XGBoost Python Feature Walkthrough
 ==================================
 * [Basic walkthrough of wrappers](basic_walkthrough.py)
-* [Customize loss function, and evaluation metric](custom_objective.py)
 * [Re-implement RMSLE as customized metric and objective](custom_rmsle.py)
 * [Re-Implement `multi:softmax` objective as customized objective](custom_softmax.py)
 * [Boosting from existing prediction](boost_from_prediction.py)
diff --git a/demo/guide-python/custom_objective.py b/demo/guide-python/custom_objective.py
deleted file mode 100644
index 6fa8bab52..000000000
--- a/demo/guide-python/custom_objective.py
+++ /dev/null
@@ -1,61 +0,0 @@
-###
-# advanced: customized loss function
-#
-import os
-import numpy as np
-import xgboost as xgb
-
-print('start running example to used customized objective function')
-
-CURRENT_DIR = os.path.dirname(__file__)
-dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
-dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
-
-# note: what we are getting is margin value in prediction you must know what
-# you are doing
-param = {'max_depth': 2, 'eta': 1, 'objective': 'reg:logistic'}
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 10
-
-
-# user define objective function, given prediction, return gradient and second
-# order gradient this is log likelihood loss
-def logregobj(preds, dtrain):
-    labels = dtrain.get_label()
-    preds = 1.0 / (1.0 + np.exp(-preds))  # transform raw leaf weight
-    grad = preds - labels
-    hess = preds * (1.0 - preds)
-    return grad, hess
-
-
-# user defined evaluation function, return a pair metric_name, result
-
-# NOTE: when you do customized loss function, the default prediction value is
-# margin, which means the prediction is score before logistic transformation.
-def evalerror(preds, dtrain):
-    labels = dtrain.get_label()
-    preds = 1.0 / (1.0 + np.exp(-preds))  # transform raw leaf weight
-    # return a pair metric_name, result. The metric name must not contain a
-    # colon (:) or a space
-    return 'my-error', float(sum(labels != (preds > 0.5))) / len(labels)
-
-
-py_evals_result = {}
-
-# training with customized objective, we can also do step by step training
-# simply look at training.py's implementation of train
-py_params = param.copy()
-py_params.update({'disable_default_eval_metric': True})
-py_logreg = xgb.train(py_params, dtrain, num_round, watchlist, obj=logregobj,
-                      feval=evalerror, evals_result=py_evals_result)
-
-evals_result = {}
-params = param.copy()
-params.update({'eval_metric': 'error'})
-logreg = xgb.train(params, dtrain, num_boost_round=num_round, evals=watchlist,
-                   evals_result=evals_result)
-
-
-for i in range(len(py_evals_result['train']['my-error'])):
-    np.testing.assert_almost_equal(py_evals_result['train']['my-error'],
-                                   evals_result['train']['error'])
diff --git a/tests/python/test_demos.py b/tests/python/test_demos.py
index 2df773d22..e4d1b804c 100644
--- a/tests/python/test_demos.py
+++ b/tests/python/test_demos.py
@@ -87,12 +87,6 @@ def test_generalized_linear_model_demo():
     subprocess.check_call(cmd)
 
 
-def test_custom_objective_demo():
-    script = os.path.join(PYTHON_DEMO_DIR, 'custom_objective.py')
-    cmd = ['python', script]
-    subprocess.check_call(cmd)
-
-
 def test_cross_validation_demo():
     script = os.path.join(PYTHON_DEMO_DIR, 'cross_validation.py')
     cmd = ['python', script]