diff --git a/tests/python/test_models.py b/tests/python/test_models.py
new file mode 100644
index 000000000..8c06d9de9
--- /dev/null
+++ b/tests/python/test_models.py
@@ -0,0 +1,39 @@
+import numpy as np
+import xgboost as xgb
+
+dpath = 'demo/data/'
+dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
+dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
+
+def test_glm():
+	param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
+	watchlist  = [(dtest,'eval'), (dtrain,'train')]
+	num_round = 4
+	bst = xgb.train(param, dtrain, num_round, watchlist)
+	assert isinstance(bst, xgb.core.Booster)
+	preds = bst.predict(dtest)
+	labels = dtest.get_label()
+	err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
+	assert err < 0.1
+
+def test_custom_objective():
+	param = {'max_depth':2, 'eta':1, 'silent':1 }
+	watchlist  = [(dtest,'eval'), (dtrain,'train')]
+	num_round = 2
+	def logregobj(preds, dtrain):
+		labels = dtrain.get_label()
+		preds = 1.0 / (1.0 + np.exp(-preds))
+		grad = preds - labels
+		hess = preds * (1.0-preds)
+		return grad, hess
+	def evalerror(preds, dtrain):
+		labels = dtrain.get_label()
+		return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
+	bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
+	assert isinstance(bst, xgb.core.Booster)
+	preds = bst.predict(dtest)
+	labels = dtest.get_label()
+	err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
+	assert err < 0.1
+
+