diff --git a/demo/guide-python/predict_first_ntree.py b/demo/guide-python/predict_first_ntree.py
index d542c55b7..a663e672a 100644
--- a/demo/guide-python/predict_first_ntree.py
+++ b/demo/guide-python/predict_first_ntree.py
@@ -1,21 +1,54 @@
 import os
 import numpy as np
 import xgboost as xgb
+from sklearn.datasets import load_svmlight_file
 
-# load data in do training
 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'))
-param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
-watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-num_round = 3
-bst = xgb.train(param, dtrain, num_round, watchlist)
+train = os.path.join(CURRENT_DIR, "../data/agaricus.txt.train")
+test = os.path.join(CURRENT_DIR, "../data/agaricus.txt.test")
 
-print('start testing prediction from first n trees')
-# predict using first 1 tree
-label = dtest.get_label()
-ypred1 = bst.predict(dtest, ntree_limit=1)
-# by default, we predict using all the trees
-ypred2 = bst.predict(dtest)
-print('error of ypred1=%f' % (np.sum((ypred1 > 0.5) != label) / float(len(label))))
-print('error of ypred2=%f' % (np.sum((ypred2 > 0.5) != label) / float(len(label))))
+
+def native_interface():
+    # load data in do training
+    dtrain = xgb.DMatrix(train)
+    dtest = xgb.DMatrix(test)
+    param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
+    watchlist = [(dtest, "eval"), (dtrain, "train")]
+    num_round = 3
+    bst = xgb.train(param, dtrain, num_round, watchlist)
+
+    print("start testing prediction from first n trees")
+    # predict using first 1 tree
+    label = dtest.get_label()
+    ypred1 = bst.predict(dtest, iteration_range=(0, 1))
+    # by default, we predict using all the trees
+    ypred2 = bst.predict(dtest)
+
+    print("error of ypred1=%f" % (np.sum((ypred1 > 0.5) != label) / float(len(label))))
+    print("error of ypred2=%f" % (np.sum((ypred2 > 0.5) != label) / float(len(label))))
+
+
+def sklearn_interface():
+    X_train, y_train = load_svmlight_file(train)
+    X_test, y_test = load_svmlight_file(test)
+    clf = xgb.XGBClassifier(n_estimators=3, max_depth=2, eta=1, use_label_encoder=False)
+    clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
+    assert clf.n_classes_ == 2
+
+    print("start testing prediction from first n trees")
+    # predict using first 1 tree
+    ypred1 = clf.predict(X_test, iteration_range=(0, 1))
+    # by default, we predict using all the trees
+    ypred2 = clf.predict(X_test)
+
+    print(
+        "error of ypred1=%f" % (np.sum((ypred1 > 0.5) != y_test) / float(len(y_test)))
+    )
+    print(
+        "error of ypred2=%f" % (np.sum((ypred2 > 0.5) != y_test) / float(len(y_test)))
+    )
+
+
+if __name__ == "__main__":
+    native_interface()
+    sklearn_interface()