diff --git a/src/mlpack/tests/serialization_test.cpp b/src/mlpack/tests/serialization_test.cpp
index 8ca7b00ebd..d8f54ca9ae 100644
--- a/src/mlpack/tests/serialization_test.cpp
+++ b/src/mlpack/tests/serialization_test.cpp
@@ -891,63 +891,63 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTest)
 //   }
 // }
 
-// BOOST_AUTO_TEST_CASE(NaiveBayesSerializationTest)
-// {
-//   // Train NBC randomly.  Make sure the model is the same after serializing and
-//   // re-loading.
-//   arma::mat dataset;
-//   dataset.randu(10, 500);
-//   arma::Row<size_t> labels(500);
-//   for (size_t i = 0; i < 500; ++i)
-//   {
-//     if (dataset(0, i) > 0.5)
-//       labels[i] = 0;
-//     else
-//       labels[i] = 1;
-//   }
+BOOST_AUTO_TEST_CASE(NaiveBayesSerializationTest)
+{
+  // Train NBC randomly.  Make sure the model is the same after serializing and
+  // re-loading.
+  arma::mat dataset;
+  dataset.randu(10, 500);
+  arma::Row<size_t> labels(500);
+  for (size_t i = 0; i < 500; ++i)
+  {
+    if (dataset(0, i) > 0.5)
+      labels[i] = 0;
+    else
+      labels[i] = 1;
+  }
 
-//   NaiveBayesClassifier<> nbc(dataset, labels, 2);
+  NaiveBayesClassifier<> nbc(dataset, labels, 2);
 
-//   // Initialize some empty Naive Bayes classifiers.
-//   NaiveBayesClassifier<> xmlNbc(0, 0), textNbc(0, 0), binaryNbc(0, 0);
-//   SerializeObjectAll(nbc, xmlNbc, textNbc, binaryNbc);
+  // Initialize some empty Naive Bayes classifiers.
+  NaiveBayesClassifier<> xmlNbc(0, 0), textNbc(0, 0), binaryNbc(0, 0);
+  SerializeObjectAll(nbc, xmlNbc, textNbc, binaryNbc);
 
-//   BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, xmlNbc.Means().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, textNbc.Means().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, binaryNbc.Means().n_elem);
-//   for (size_t i = 0; i < nbc.Means().n_elem; ++i)
-//   {
-//     BOOST_REQUIRE_CLOSE(nbc.Means()[i], xmlNbc.Means()[i], 1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Means()[i], textNbc.Means()[i], 1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Means()[i], binaryNbc.Means()[i], 1e-5);
-//   }
+  BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, xmlNbc.Means().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, textNbc.Means().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Means().n_elem, binaryNbc.Means().n_elem);
+  for (size_t i = 0; i < nbc.Means().n_elem; ++i)
+  {
+    BOOST_REQUIRE_CLOSE(nbc.Means()[i], xmlNbc.Means()[i], 1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Means()[i], textNbc.Means()[i], 1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Means()[i], binaryNbc.Means()[i], 1e-5);
+  }
 
-//   BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, xmlNbc.Variances().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, textNbc.Variances().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, binaryNbc.Variances().n_elem);
-//   for (size_t i = 0; i < nbc.Variances().n_elem; ++i)
-//   {
-//     BOOST_REQUIRE_CLOSE(nbc.Variances()[i], xmlNbc.Variances()[i], 1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Variances()[i], textNbc.Variances()[i], 1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Variances()[i], binaryNbc.Variances()[i], 1e-5);
-//   }
+  BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, xmlNbc.Variances().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, textNbc.Variances().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Variances().n_elem, binaryNbc.Variances().n_elem);
+  for (size_t i = 0; i < nbc.Variances().n_elem; ++i)
+  {
+    BOOST_REQUIRE_CLOSE(nbc.Variances()[i], xmlNbc.Variances()[i], 1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Variances()[i], textNbc.Variances()[i], 1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Variances()[i], binaryNbc.Variances()[i], 1e-5);
+  }
 
-//   BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
-//       xmlNbc.Probabilities().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
-//       textNbc.Probabilities().n_elem);
-//   BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
-//       binaryNbc.Probabilities().n_elem);
-//   for (size_t i = 0; i < nbc.Probabilities().n_elem; ++i)
-//   {
-//     BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], xmlNbc.Probabilities()[i],
-//         1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], textNbc.Probabilities()[i],
-//         1e-5);
-//     BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], binaryNbc.Probabilities()[i],
-//         1e-5);
-//   }
-// }
+  BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
+      xmlNbc.Probabilities().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
+      textNbc.Probabilities().n_elem);
+  BOOST_REQUIRE_EQUAL(nbc.Probabilities().n_elem,
+      binaryNbc.Probabilities().n_elem);
+  for (size_t i = 0; i < nbc.Probabilities().n_elem; ++i)
+  {
+    BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], xmlNbc.Probabilities()[i],
+        1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], textNbc.Probabilities()[i],
+        1e-5);
+    BOOST_REQUIRE_CLOSE(nbc.Probabilities()[i], binaryNbc.Probabilities()[i],
+        1e-5);
+  }
+}
 
 // BOOST_AUTO_TEST_CASE(RASearchTest)
 // {
@@ -1065,41 +1065,41 @@ BOOST_AUTO_TEST_CASE(SoftmaxRegressionTest)
 //       textLsh.SecondHashTable()[i], binaryLsh.SecondHashTable()[i]);
 // }
 
-// // Make sure serialization works for the decision stump.
-// BOOST_AUTO_TEST_CASE(DecisionStumpTest)
-// {
-//   // Generate dataset.
-//   arma::mat trainingData = arma::randu<arma::mat>(4, 100);
-//   arma::Row<size_t> labels(100);
-//   for (size_t i = 0; i < 25; ++i)
-//     labels[i] = 0;
-//   for (size_t i = 25; i < 50; ++i)
-//     labels[i] = 3;
-//   for (size_t i = 50; i < 75; ++i)
-//     labels[i] = 1;
-//   for (size_t i = 75; i < 100; ++i)
-//     labels[i] = 2;
+// Make sure serialization works for the decision stump.
+BOOST_AUTO_TEST_CASE(DecisionStumpTest)
+{
+  // Generate dataset.
+  arma::mat trainingData = arma::randu<arma::mat>(4, 100);
+  arma::Row<size_t> labels(100);
+  for (size_t i = 0; i < 25; ++i)
+    labels[i] = 0;
+  for (size_t i = 25; i < 50; ++i)
+    labels[i] = 3;
+  for (size_t i = 50; i < 75; ++i)
+    labels[i] = 1;
+  for (size_t i = 75; i < 100; ++i)
+    labels[i] = 2;
 
-//   DecisionStump<> ds(trainingData, labels, 4, 3);
+  DecisionStump<> ds(trainingData, labels, 4, 3);
 
-//   arma::mat otherData = arma::randu<arma::mat>(3, 100);
-//   arma::Row<size_t> otherLabels = arma::randu<arma::Row<size_t>>(100);
-//   DecisionStump<> xmlDs(otherData, otherLabels, 2, 3);
+  arma::mat otherData = arma::randu<arma::mat>(3, 100);
+  arma::Row<size_t> otherLabels = arma::randu<arma::Row<size_t>>(100);
+  DecisionStump<> xmlDs(otherData, otherLabels, 2, 3);
 
-//   DecisionStump<> textDs;
-//   DecisionStump<> binaryDs(trainingData, labels, 4, 10);
+  DecisionStump<> textDs;
+  DecisionStump<> binaryDs(trainingData, labels, 4, 10);
 
-//   SerializeObjectAll(ds, xmlDs, textDs, binaryDs);
+  SerializeObjectAll(ds, xmlDs, textDs, binaryDs);
 
-//   // Make sure that everything is the same about the new decision stumps.
-//   BOOST_REQUIRE_EQUAL(ds.SplitDimension(), xmlDs.SplitDimension());
-//   BOOST_REQUIRE_EQUAL(ds.SplitDimension(), textDs.SplitDimension());
-//   BOOST_REQUIRE_EQUAL(ds.SplitDimension(), binaryDs.SplitDimension());
+  // Make sure that everything is the same about the new decision stumps.
+  BOOST_REQUIRE_EQUAL(ds.SplitDimension(), xmlDs.SplitDimension());
+  BOOST_REQUIRE_EQUAL(ds.SplitDimension(), textDs.SplitDimension());
+  BOOST_REQUIRE_EQUAL(ds.SplitDimension(), binaryDs.SplitDimension());
 
-//   CheckMatrices(ds.Split(), xmlDs.Split(), textDs.Split(), binaryDs.Split());
-//   CheckMatrices(ds.BinLabels(), xmlDs.BinLabels(), textDs.BinLabels(),
-//       binaryDs.BinLabels());
-// }
+  CheckMatrices(ds.Split(), xmlDs.Split(), textDs.Split(), binaryDs.Split());
+  CheckMatrices(ds.BinLabels(), xmlDs.BinLabels(), textDs.BinLabels(),
+      binaryDs.BinLabels());
+}
 
 // // Make sure serialization works for LARS.
 // BOOST_AUTO_TEST_CASE(LARSTest)