diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 2db5dce8b143a..d4771d779f9f4 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -25,6 +25,7 @@
 from pyspark.mllib._common import _convert_vector, _serialize_double_vector, \
         _deserialize_double_vector, _dot, _squared_distance
 from pyspark.mllib.linalg import SparseVector
+from pyspark.mllib.regression import LabeledPoint
 from pyspark.tests import PySparkTestCase
 
 
@@ -41,16 +42,21 @@ class VectorTests(unittest.TestCase):
     def test_serialize(self):
         sv = SparseVector(4, {1: 1, 3: 2})
         dv = array([1., 2., 3., 4.])
+        lst = [1, 2, 3, 4]
         self.assertTrue(sv is _convert_vector(sv))
         self.assertTrue(dv is _convert_vector(dv))
+        self.assertTrue(array_equal(dv, _convert_vector(lst)))
         self.assertEquals(sv,
                 _deserialize_double_vector(_serialize_double_vector(sv)))
         self.assertTrue(array_equal(dv,
                 _deserialize_double_vector(_serialize_double_vector(dv))))
+        self.assertTrue(array_equal(dv,
+                _deserialize_double_vector(_serialize_double_vector(lst))))
 
     def test_dot(self):
         sv = SparseVector(4, {1: 1, 3: 2})
         dv = array([1., 2., 3., 4.])
+        lst = [1, 2, 3, 4]
         mat = array([[1., 2., 3., 4.],
                      [1., 2., 3., 4.],
                      [1., 2., 3., 4.],
@@ -59,10 +65,109 @@ def test_dot(self):
         self.assertTrue(array_equal(array([3., 6., 9., 12.]), _dot(sv, mat)))
         self.assertEquals(30.0, _dot(dv, dv))
         self.assertTrue(array_equal(array([10., 20., 30., 40.]), _dot(dv, mat)))
+        self.assertEquals(30.0, _dot(lst, dv))
+        self.assertTrue(array_equal(array([10., 20., 30., 40.]), _dot(lst, mat)))
+
+    def test_squared_distance(self):
+        sv = SparseVector(4, {1: 1, 3: 2})
+        dv = array([1., 2., 3., 4.])
+        lst = [4, 3, 2, 1]
+        self.assertEquals(15.0, _squared_distance(sv, dv))
+        self.assertEquals(25.0, _squared_distance(sv, lst))
+        self.assertEquals(20.0, _squared_distance(dv, lst))
+        self.assertEquals(15.0, _squared_distance(dv, sv))
+        self.assertEquals(25.0, _squared_distance(lst, sv))
+        self.assertEquals(20.0, _squared_distance(lst, dv))
+        self.assertEquals(0.0, _squared_distance(sv, sv))
+        self.assertEquals(0.0, _squared_distance(dv, dv))
+        self.assertEquals(0.0, _squared_distance(lst, lst))
+
+
+class ListTests(PySparkTestCase):
+    """
+    Test MLlib algorithms on plain lists, to make sure they're passed through
+    as NumPy arrays.
+    """
+
+    def test_clustering(self):
+        from pyspark.mllib.clustering import KMeans
+        data = [
+            [0, 1.1],
+            [0, 1.2],
+            [1.1, 0],
+            [1.2, 0],
+        ]
+        clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||")
+        self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1]))
+        self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3]))
+
+    def test_classification(self):
+        from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
+        data = [
+            LabeledPoint(0.0, [1, 0]),
+            LabeledPoint(1.0, [0, 1]),
+            LabeledPoint(0.0, [2, 0]),
+            LabeledPoint(1.0, [0, 2])
+        ]
+        rdd = self.sc.parallelize(data)
+        features = [p.features.tolist() for p in data]
+
+        lr_model = LogisticRegressionWithSGD.train(rdd)
+        self.assertTrue(lr_model.predict(features[0]) <= 0)
+        self.assertTrue(lr_model.predict(features[1]) > 0)
+        self.assertTrue(lr_model.predict(features[2]) <= 0)
+        self.assertTrue(lr_model.predict(features[3]) > 0)
+
+        svm_model = SVMWithSGD.train(rdd)
+        self.assertTrue(svm_model.predict(features[0]) <= 0)
+        self.assertTrue(svm_model.predict(features[1]) > 0)
+        self.assertTrue(svm_model.predict(features[2]) <= 0)
+        self.assertTrue(svm_model.predict(features[3]) > 0)
+
+        nb_model = NaiveBayes.train(rdd)
+        self.assertTrue(nb_model.predict(features[0]) <= 0)
+        self.assertTrue(nb_model.predict(features[1]) > 0)
+        self.assertTrue(nb_model.predict(features[2]) <= 0)
+        self.assertTrue(nb_model.predict(features[3]) > 0)
+
+    def test_regression(self):
+        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
+                RidgeRegressionWithSGD
+        data = [
+            LabeledPoint(-1.0, [0, -1]),
+            LabeledPoint(1.0, [0, 1]),
+            LabeledPoint(-1.0, [0, -2]),
+            LabeledPoint(1.0, [0, 2])
+        ]
+        rdd = self.sc.parallelize(data)
+        features = [p.features.tolist() for p in data]
+
+        lr_model = LinearRegressionWithSGD.train(rdd)
+        self.assertTrue(lr_model.predict(features[0]) <= 0)
+        self.assertTrue(lr_model.predict(features[1]) > 0)
+        self.assertTrue(lr_model.predict(features[2]) <= 0)
+        self.assertTrue(lr_model.predict(features[3]) > 0)
+
+        lasso_model = LassoWithSGD.train(rdd)
+        self.assertTrue(lasso_model.predict(features[0]) <= 0)
+        self.assertTrue(lasso_model.predict(features[1]) > 0)
+        self.assertTrue(lasso_model.predict(features[2]) <= 0)
+        self.assertTrue(lasso_model.predict(features[3]) > 0)
+
+        rr_model = RidgeRegressionWithSGD.train(rdd)
+        self.assertTrue(rr_model.predict(features[0]) <= 0)
+        self.assertTrue(rr_model.predict(features[1]) > 0)
+        self.assertTrue(rr_model.predict(features[2]) <= 0)
+        self.assertTrue(rr_model.predict(features[3]) > 0)
 
 
 @unittest.skipIf(not _have_scipy, "SciPy not installed")
 class SciPyTests(PySparkTestCase):
+    """
+    Test both vector operations and MLlib algorithms with SciPy sparse matrices,
+    if SciPy is available.
+    """
+
     def test_serialize(self):
         from scipy.sparse import lil_matrix
         lil = lil_matrix((4, 1))
@@ -132,63 +237,61 @@ def test_clustering(self):
     def test_classification(self):
         from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
         data = [
-            self.scipy_matrix(3, {0: 0.0, 2: 0.0}),
-            self.scipy_matrix(3, {0: 1.0, 2: 1.0}),
-            self.scipy_matrix(3, {0: 0.0, 2: 0.0}),
-            self.scipy_matrix(3, {0: 1.0, 2: 2.0})
+            LabeledPoint(0.0, self.scipy_matrix(2, {0: 1.0})),
+            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
+            LabeledPoint(0.0, self.scipy_matrix(2, {0: 2.0})),
+            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
         ]
         rdd = self.sc.parallelize(data)
-        features = [
-            self.scipy_matrix(2, {1: 0.0}),
-            self.scipy_matrix(2, {1: 1.0}),
-            self.scipy_matrix(2, {1: 2.0}),
-        ]
+        features = [p.features for p in data]
 
         lr_model = LogisticRegressionWithSGD.train(rdd)
         self.assertTrue(lr_model.predict(features[0]) <= 0)
         self.assertTrue(lr_model.predict(features[1]) > 0)
-        self.assertTrue(lr_model.predict(features[2]) > 0)
+        self.assertTrue(lr_model.predict(features[2]) <= 0)
+        self.assertTrue(lr_model.predict(features[3]) > 0)
 
         svm_model = SVMWithSGD.train(rdd)
         self.assertTrue(svm_model.predict(features[0]) <= 0)
         self.assertTrue(svm_model.predict(features[1]) > 0)
-        self.assertTrue(svm_model.predict(features[2]) > 0)
+        self.assertTrue(svm_model.predict(features[2]) <= 0)
+        self.assertTrue(svm_model.predict(features[3]) > 0)
 
         nb_model = NaiveBayes.train(rdd)
         self.assertTrue(nb_model.predict(features[0]) <= 0)
         self.assertTrue(nb_model.predict(features[1]) > 0)
-        self.assertTrue(nb_model.predict(features[2]) > 0)
+        self.assertTrue(nb_model.predict(features[2]) <= 0)
+        self.assertTrue(nb_model.predict(features[3]) > 0)
 
     def test_regression(self):
         from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
                 RidgeRegressionWithSGD
         data = [
-            self.scipy_matrix(3, {0: -1.0, 2: -1.0}),
-            self.scipy_matrix(3, {0: 1.0, 2: 1.0}),
-            self.scipy_matrix(3, {0: -1.0, 2: -2.0}),
-            self.scipy_matrix(3, {0: 1.0, 2: 2.0})
+            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
+            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
+            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -2.0})),
+            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
         ]
         rdd = self.sc.parallelize(data)
-        features = [
-            self.scipy_matrix(2, {1: -1.0}),
-            self.scipy_matrix(2, {1: 1.0}),
-            self.scipy_matrix(2, {1: 2.0}),
-        ]
+        features = [p.features for p in data]
 
         lr_model = LinearRegressionWithSGD.train(rdd)
         self.assertTrue(lr_model.predict(features[0]) <= 0)
         self.assertTrue(lr_model.predict(features[1]) > 0)
-        self.assertTrue(lr_model.predict(features[2]) > 0)
+        self.assertTrue(lr_model.predict(features[2]) <= 0)
+        self.assertTrue(lr_model.predict(features[3]) > 0)
 
         lasso_model = LassoWithSGD.train(rdd)
         self.assertTrue(lasso_model.predict(features[0]) <= 0)
         self.assertTrue(lasso_model.predict(features[1]) > 0)
-        self.assertTrue(lasso_model.predict(features[2]) > 0)
+        self.assertTrue(lasso_model.predict(features[2]) <= 0)
+        self.assertTrue(lasso_model.predict(features[3]) > 0)
 
         rr_model = RidgeRegressionWithSGD.train(rdd)
         self.assertTrue(rr_model.predict(features[0]) <= 0)
         self.assertTrue(rr_model.predict(features[1]) > 0)
-        self.assertTrue(rr_model.predict(features[2]) > 0)
+        self.assertTrue(rr_model.predict(features[2]) <= 0)
+        self.assertTrue(rr_model.predict(features[3]) > 0)
 
 
 if __name__ == "__main__":
diff --git a/python/run-tests b/python/run-tests
index abd5efcf69a94..7bbf10d05a817 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -60,6 +60,7 @@ run_test "pyspark/mllib/clustering.py"
 run_test "pyspark/mllib/linalg.py"
 run_test "pyspark/mllib/recommendation.py"
 run_test "pyspark/mllib/regression.py"
+run_test "pyspark/mllib/tests.py"
 
 if [[ $FAILED == 0 ]]; then
     echo -en "\033[32m"  # Green