diff --git a/demo/guide-python/sklearn_examples.py b/demo/guide-python/sklearn_examples.py
index ce8c8d01e..56fed1dd2 100755
--- a/demo/guide-python/sklearn_examples.py
+++ b/demo/guide-python/sklearn_examples.py
@@ -8,7 +8,7 @@ import pickle
 import xgboost as xgb
 
 import numpy as np
-from sklearn.cross_validation import KFold
+from sklearn.cross_validation import KFold, train_test_split
 from sklearn.metrics import confusion_matrix, mean_squared_error
 from sklearn.grid_search import GridSearchCV
 from sklearn.datasets import load_iris, load_digits, load_boston
@@ -65,3 +65,23 @@ print("Pickling sklearn API models")
 pickle.dump(clf, open("best_boston.pkl", "wb"))
 clf2 = pickle.load(open("best_boston.pkl", "rb"))
 print(np.allclose(clf.predict(X), clf2.predict(X)))
+
+# Early-stopping
+
+X = digits['data']
+y = digits['target']
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+clf = xgb.XGBClassifier()
+clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
+        eval_set=[(X_test, y_test)])
+
+# Custom evaluation function
+from sklearn.metrics import log_loss
+
+
+def log_loss_eval(y_pred, y_true):
+    return "log-loss", log_loss(y_true.get_label(), y_pred)
+
+
+clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=log_loss_eval,
+        eval_set=[(X_test, y_test)])