diff --git a/HISTORY.md b/HISTORY.md
index 52c2388d62..7cd82d907b 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -4,7 +4,12 @@
 
   * Fix bug in LogSoftMax derivative (#3469).
 
-  * Add `serialize` method to `GaussianInitialization`, `KathirvalavakumarSubavathiInitialization`, `KathirvalavakumarSubavathiInitialization`, `NguyenWidrowInitialization`, and `OrthogonalInitialization` (#3483).
+  * Add `serialize` method to `GaussianInitialization`,
+    `KathirvalavakumarSubavathiInitialization`,
+    `KathirvalavakumarSubavathiInitialization`, `NguyenWidrowInitialization`,
+    and `OrthogonalInitialization` (#3483).
+
+  * Allow categorical features to `preprocess_one_hot_encode` (#3487).
 
   * Install mlpack and cereal headers as part of R package (#3488).
 
diff --git a/src/mlpack/bindings/python/print_input_processing.hpp b/src/mlpack/bindings/python/print_input_processing.hpp
index 2cdd01fc4b..99e0d54a72 100644
--- a/src/mlpack/bindings/python/print_input_processing.hpp
+++ b/src/mlpack/bindings/python/print_input_processing.hpp
@@ -488,9 +488,9 @@ void PrintInputProcessing(
   {
     std::cout << prefix << "cdef extern from \"numpy/arrayobject.h\":" << std::endl;
     std::cout << prefix << "  void* PyArray_DATA(np.ndarray arr)" << std::endl;
-    std::cout << prefix << "if " << d.name << " is not None:" << std::endl;
-    std::cout << prefix << "  " << d.name << "_tuple = to_matrix_with_info("
-        << d.name << ", dtype=np.double, copy=p.Has('copy_all_inputs'))"
+    std::cout << prefix << "if " << name << " is not None:" << std::endl;
+    std::cout << prefix << "  " << name << "_tuple = to_matrix_with_info("
+        << name << ", dtype=np.double, copy=p.Has('copy_all_inputs'))"
         << std::endl;
     std::cout << prefix << "  if len(" << name << "_tuple[0].shape"
         << ") < 2:" << std::endl;
@@ -501,8 +501,8 @@ void PrintInputProcessing(
     std::cout << prefix << "  " << name << "_dims = " << name
         << "_tuple[2]" << std::endl;
     std::cout << prefix << "  SetParamWithInfo[arma.Mat[double]](p, <const "
-        << "string> '" << d.name << "', dereference(" << d.name << "_mat), "
-        << "<const cbool*> PyArray_DATA(" << d.name << "_dims))" << std::endl;
+        << "string> '" << d.name << "', dereference(" << name << "_mat), "
+        << "<const cbool*> PyArray_DATA(" << name << "_dims))" << std::endl;
     std::cout << prefix << "  p.SetPassed(<const string> '" << d.name
         << "')" << std::endl;
     std::cout << prefix << "  del " << name << "_mat" << std::endl;
@@ -511,7 +511,7 @@ void PrintInputProcessing(
   {
     std::cout << prefix << "cdef extern from \"numpy/arrayobject.h\":" << std::endl;
     std::cout << prefix << "  void* PyArray_DATA(np.ndarray arr)" << std::endl;
-    std::cout << prefix << d.name << "_tuple = to_matrix_with_info(" << d.name
+    std::cout << prefix << name << "_tuple = to_matrix_with_info(" << name
         << ", dtype=np.double, copy=p.Has('copy_all_inputs'))"
         << std::endl;
     std::cout << prefix << "if len(" << name << "_tuple[0].shape"
@@ -523,8 +523,8 @@ void PrintInputProcessing(
     std::cout << prefix << name << "_dims = " << name << "_tuple[2]"
         << std::endl;
     std::cout << prefix << "SetParamWithInfo[arma.Mat[double]](p, <const "
-        << "string> '" << d.name << "', dereference(" << d.name << "_mat), "
-        << "<const cbool*> PyArray_DATA(" << d.name << "_dims))" << std::endl;
+        << "string> '" << d.name << "', dereference(" << name << "_mat), "
+        << "<const cbool*> PyArray_DATA(" << name << "_dims))" << std::endl;
     std::cout << prefix << "p.SetPassed(<const string> '" << d.name << "')"
         << std::endl;
     std::cout << prefix << "del " << name << "_mat" << std::endl;
diff --git a/src/mlpack/core/util/param.hpp b/src/mlpack/core/util/param.hpp
index 213982e1ab..838b2063af 100644
--- a/src/mlpack/core/util/param.hpp
+++ b/src/mlpack/core/util/param.hpp
@@ -330,7 +330,7 @@
 
 /**
  * Define a matrix output parameter.  When the program terminates, the matrix
- * will be saved to whatever it was set to by IO::GetParam<arma::mat>(ID)
+ * will be saved to whatever it was set to by params.Get<arma::mat>(ID)
  * during the program.  From the command-line, the user may specify the file in
  * which to save the output matrix using a string option that is the name of the
  * matrix parameter with "_file" appended.  So, for instance, if the name of the
@@ -400,7 +400,7 @@
  * Define a transposed matrix output parameter.  This is useful when data is
  * stored in a row-major form instead of the usual column-major form.  When the
  * program terminates, the matrix will be saved to whatever it was set to by
- * IO::GetParam<arma::mat>(ID) during the program.  From the command-line, the
+ * params.Get<arma::mat>(ID) during the program.  From the command-line, the
  * user may specify the file in which to save the output matrix using a string
  * option that is the name of the matrix parameter with "_file" appended.  So,
  * for instance, if the name of the output matrix parameter was "mat", the user
@@ -467,7 +467,7 @@
 /**
  * Define an unsigned matrix output parameter (arma::Mat<size_t>).  When the
  * program terminates, the matrix will be saved to whatever it was set to by
- * IO::GetParam<arma::Mat<size_t>>(ID) during the program.  From the
+ * params.Get<arma::Mat<size_t>>(ID) during the program.  From the
  * command-line, the user may specify the file in which to save the output
  * matrix using a string option that is the name of the matrix parameter with
  * "_file" appended.  So, for instance, if the name of the output matrix
@@ -746,9 +746,9 @@
  *
  * @code
  * DatasetInfo d = std::move(
- *     IO::GetParam<std::tuple<DatasetInfo, arma::mat>>("matrix").get<0>());
+ *     params.Get<std::tuple<DatasetInfo, arma::mat>>("matrix").get<0>());
  * arma::mat m = std::move(
- *     IO::GetParam<std::tuple<DatasetInfo, arma::mat>>("matrix").get<1>());
+ *     params.Get<std::tuple<DatasetInfo, arma::mat>>("matrix").get<1>());
  * @endcode
  *
  * @param ID Name of the parameter.
@@ -763,6 +763,38 @@
         "std::tuple<mlpack::data::DatasetInfo, arma::mat>", false, true, true, \
         TUPLE_TYPE())
 
+/**
+ * Define a required input DatasetInfo/matrix parameter.  From the command line,
+ * the user can specify the file that holds the matrix, using the name of the
+ * matrix parameter with "_file" appended (and the same alias).  So for
+ * instance, if the name of the matrix parameter was "matrix", the user could
+ * specify that the "matrix" matrix was held in file.csv by giving the parameter
+ *
+ * @code
+ * --matrix_file file.csv
+ * @endcode
+ *
+ * Then the DatasetInfo and matrix type could be accessed with
+ *
+ * @code
+ * DatasetInfo d = std::move(
+ *     params.Get<std::tuple<DatasetInfo, arma::mat>>("matrix").get<0>());
+ * arma::mat m = std::move(
+ *     params.Get<std::tuple<DatasetInfo, arma::mat>>("matrix").get<1>());
+ * @endcode
+ *
+ * @param ID Name of the parameter.
+ * @param DESC Quick description of the parameter (1-2 sentences).  Don't use
+ *      printing macros like PRINT_PARAM_STRING() or PRINT_DATASET() or others
+ *      here---it will cause problems.
+ * @param ALIAS One-character string representing the alias of the parameter.
+ */
+#define TUPLE_TYPE std::tuple<mlpack::data::DatasetInfo, arma::mat>
+#define PARAM_MATRIX_AND_INFO_IN_REQ(ID, DESC, ALIAS) \
+    PARAM(TUPLE_TYPE, ID, DESC, ALIAS, \
+        "std::tuple<mlpack::data::DatasetInfo, arma::mat>", true, true, true, \
+        TUPLE_TYPE())
+
 /**
  * Define an input model.  From the command line, the user can specify the file
  * that holds the model, using the name of the model parameter with "_file"
@@ -836,9 +868,7 @@
  * --model_file model.bin
  * @endcode
  *
- * The model will be saved at the termination of the program.  If you use a
- * parameter of this type, you must call IO::Destroy() at the end of your
- * program.
+ * The model will be saved at the termination of the program.
  *
  * @param TYPE Type of the model to be saved.
  * @param ID Name of the parameter.
diff --git a/src/mlpack/methods/preprocess/preprocess_one_hot_encoding_main.cpp b/src/mlpack/methods/preprocess/preprocess_one_hot_encoding_main.cpp
index 3b7f1b3e1c..49952872b7 100644
--- a/src/mlpack/methods/preprocess/preprocess_one_hot_encoding_main.cpp
+++ b/src/mlpack/methods/preprocess/preprocess_one_hot_encoding_main.cpp
@@ -28,6 +28,11 @@ BINDING_LONG_DESC(
     "encoding of the respective features at those indices. Indices represent "
     "the IDs of the dimensions to be one-hot encoded."
     "\n\n"
+    "If no dimensions are specified with " + PRINT_PARAM_STRING("dimensions") +
+    ", then all categorical-type dimensions will be one-hot encoded. "
+    "Otherwise, only the dimensions given in " +
+    PRINT_PARAM_STRING("dimensions") + " will be one-hot encoded."
+    "\n\n"
     "The output matrix with encoded features may be saved with the " +
     PRINT_PARAM_STRING("output") + " parameters.");
 
@@ -48,12 +53,13 @@ BINDING_SEE_ALSO("One-hot encoding on Wikipedia",
         "https://en.m.wikipedia.org/wiki/One-hot");
 
 // Define parameters for data.
-PARAM_MATRIX_IN_REQ("input", "Matrix containing data.", "i");
+PARAM_MATRIX_AND_INFO_IN_REQ("input", "Matrix containing data.", "i");
 PARAM_MATRIX_OUT("output", "Matrix to save one-hot encoded features "
     "data to.", "o");
 
-PARAM_VECTOR_IN_REQ(int, "dimensions", "Index of dimensions that"
-    "need to be one-hot encoded.", "d");
+PARAM_VECTOR_IN(int, "dimensions", "Index of dimensions that need to be one-hot"
+    " encoded (if unspecified, all categorical dimensions are one-hot "
+    "encoded).", "d");
 
 using namespace mlpack;
 using namespace mlpack::util;
@@ -63,29 +69,65 @@ using namespace std;
 void BINDING_FUNCTION(util::Params& params, util::Timers& /* timers */)
 {
   // Load the data.
-  const arma::mat& data = params.Get<arma::mat>("input");
-  vector<int>& indices = params.Get<vector<int> >("dimensions");
-  vector<size_t> copyIndices(indices.size());
-  RequireParamValue<std::vector<int>>(params, "dimensions",
-      [data](std::vector<int> x)
-      {
-        for (int dim : x)
-        {
-          if (dim < 0 || (size_t)dim > data.n_rows)
-          {
-            return false;
-          }
-        }
-        return true;
-      }, true, "dimensions must be greater than 0 and less than the number of "
-      "dimensions");
+  const std::tuple<data::DatasetInfo, arma::mat>& t =
+      params.Get<std::tuple<data::DatasetInfo, arma::mat>>("input");
 
-  for (size_t i = 0; i < indices.size(); ++i)
+  const data::DatasetInfo& info = std::get<0>(t);
+  const arma::mat& data = std::get<1>(t);
+
+  vector<int>& indices = params.Get<vector<int>>("dimensions");
+  if (!params.Has("dimensions"))
   {
-    copyIndices[i] = (size_t)indices[i];
+    // If the user did not specify any dimensions to convert, we pick all the
+    // categorical dimensions by default.
+    for (size_t d = 0; d < info.Dimensionality(); ++d)
+      if (info.Type(d) == data::Datatype::categorical)
+        indices.push_back(d);
+
+    // Print which dimensions we selected to one-hot encode.
+    if (indices.size() > 0)
+    {
+      Log::Info << "One-hot encoding categorical dimensions: [";
+      for (size_t i = 0; i < indices.size() - 1; ++i)
+        Log::Info << indices[i] << ", ";
+      Log::Info << indices[indices.size() - 1] << "]." << std::endl;
+    }
+  }
+  else
+  {
+    // If the user did specify dimensions, let's make sure they are reasonable.
+    RequireParamValue<std::vector<int>>(params, "dimensions",
+        [data](std::vector<int> x)
+        {
+          for (int dim : x)
+          {
+            if (dim < 0 || (size_t) dim > data.n_rows)
+            {
+              return false;
+            }
+          }
+          return true;
+        }, true, "dimensions must be greater than 0 and less than the number of"
+        " dimensions");
+  }
+
+  // Note that it's possible that zero dimensions are selected for one-hot
+  // encoding.
+  if (indices.size() > 0)
+  {
+    vector<size_t> copyIndices(indices.size());
+    for (size_t i = 0; i < indices.size(); ++i)
+    {
+      copyIndices[i] = (size_t)indices[i];
+    }
+
+    arma::mat output;
+    data::OneHotEncoding(data, (arma::Col<size_t>)(copyIndices), output);
+    if (params.Has("output"))
+      params.Get<arma::mat>("output") = std::move(output);
+  }
+  else if (params.Has("output"))
+  {
+    params.Get<arma::mat>("output") = data; // Copy input to output.
   }
-  arma::mat output;
-  data::OneHotEncoding(data, (arma::Col<size_t>)(copyIndices), output);
-  if (params.Has("output"))
-    params.Get<arma::mat>("output") = std::move(output);
 }
diff --git a/src/mlpack/tests/main_tests/preprocess_one_hot_encode_test.cpp b/src/mlpack/tests/main_tests/preprocess_one_hot_encode_test.cpp
index 1306da14a2..523025c772 100644
--- a/src/mlpack/tests/main_tests/preprocess_one_hot_encode_test.cpp
+++ b/src/mlpack/tests/main_tests/preprocess_one_hot_encode_test.cpp
@@ -47,7 +47,8 @@ TEST_CASE_METHOD(
            "0 1 0 0 0 0 1 0;"
            "1 1 -1 -1 -1 -1 1 1;";
 
-  SetInputParam("input", dataset);
+  data::DatasetInfo di(dataset.n_rows);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {1, 3});
   RUN_BINDING();
 
@@ -66,7 +67,8 @@ TEST_CASE_METHOD(
 {
   arma::mat dataset;
 
-  SetInputParam("input", dataset);
+  data::DatasetInfo di(dataset.n_rows);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {1, 3});
   // This will throw an error since dimensions are bigger than the matrix.
   REQUIRE_THROWS_AS(RUN_BINDING(), std::runtime_error);
@@ -86,7 +88,8 @@ TEST_CASE_METHOD(
             "-1 1 -1 -1 -1 -1 1 -1;"
             "1 1 -1 -1 -1 -1 1 1;";
 
-  SetInputParam("input", dataset);
+  data::DatasetInfo di(dataset.n_rows);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {});
   RUN_BINDING();
 
@@ -110,7 +113,8 @@ TEST_CASE_METHOD(
             "-1 1 -1 -1 -1 -1 1 -1;"
             "1 1 -1 -1 -1 -1 1 1;";
 
-  SetInputParam("input", dataset);
+  data::DatasetInfo di(dataset.n_rows);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {10000});
   // Error since dimensions are bigger than matrix.
   REQUIRE_THROWS_AS(RUN_BINDING(), std::runtime_error);
@@ -130,7 +134,8 @@ TEST_CASE_METHOD(
             "-1 1 -1 -1 -1 -1 1 -1;"
             "1 1 -1 -1 -1 -1 1 1;";
 
-  SetInputParam("input", dataset);
+  data::DatasetInfo di(dataset.n_rows);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {-10000});
   REQUIRE_THROWS_AS(RUN_BINDING(), std::runtime_error);
 }
@@ -143,8 +148,9 @@ TEST_CASE_METHOD(
     "[PreprocessOneHotEncodingMainTest][BindingTests]")
 {
   arma::mat dataset;
+  data::DatasetInfo di(dataset.n_rows);
 
-  SetInputParam("input", dataset);
+  SetInputParam("input", std::make_tuple(di, dataset));
   SetInputParam<vector<int>>("dimensions", {});
   RUN_BINDING();
 
@@ -153,3 +159,274 @@ TEST_CASE_METHOD(
   REQUIRE(dataset.n_rows == output.n_rows);
   CheckMatrices(output, dataset);
 }
+
+/**
+ * Test for a dataset with categorical features, where we one-hot encode all
+ * categorical features.
+ */
+TEST_CASE_METHOD(
+    PreprocessOneHotEncodingTestFixture, "CategoricalMatrixTest",
+    "[PreprocessOneHotEncodingMainTest][BindingTests]")
+{
+  arma::mat dataset(4, 5);
+  dataset.randu();
+
+  // Dimension 2 will be categorical.
+  dataset(2, 0) = 0;
+  dataset(2, 1) = 1;
+  dataset(2, 2) = 1;
+  dataset(2, 3) = 2;
+  dataset(2, 4) = 0;
+
+  data::DatasetInfo info(4);
+  info.Type(2) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 2);
+  (void) info.MapString<double>("1", 2);
+  (void) info.MapString<double>("2", 2);
+  REQUIRE(info.NumMappings(2) == 3);
+
+  SetInputParam("input", std::make_tuple(info, dataset));
+  RUN_BINDING();
+
+  arma::mat output = params.Get<arma::mat>("output");
+  REQUIRE(dataset.n_cols == output.n_cols);
+  REQUIRE(dataset.n_rows + 2 == output.n_rows);
+
+  // Make sure one-hot encoding was correct.
+  REQUIRE(output(2, 0) == 1);
+  REQUIRE(output(2, 1) == 0);
+  REQUIRE(output(2, 2) == 0);
+  REQUIRE(output(2, 3) == 0);
+  REQUIRE(output(2, 4) == 1);
+  REQUIRE(output(3, 0) == 0);
+  REQUIRE(output(3, 1) == 1);
+  REQUIRE(output(3, 2) == 1);
+  REQUIRE(output(3, 3) == 0);
+  REQUIRE(output(3, 4) == 0);
+  REQUIRE(output(4, 0) == 0);
+  REQUIRE(output(4, 1) == 0);
+  REQUIRE(output(4, 2) == 0);
+  REQUIRE(output(4, 3) == 1);
+  REQUIRE(output(4, 4) == 0);
+}
+
+/**
+ * Test for a dataset with no categorical features, where we don't specify the
+ * dimensions to convert.  This should convert nothing.
+ */
+TEST_CASE_METHOD(
+    PreprocessOneHotEncodingTestFixture, "NoCategoricalMatrixTest",
+    "[PreprocessOneHotEncodingMainTest][BindingTests]")
+{
+  arma::mat dataset(4, 5);
+  dataset.randu();
+  data::DatasetInfo info(4); // all numeric dimensions
+
+  SetInputParam("input", std::make_tuple(info, dataset));
+  RUN_BINDING();
+
+  arma::mat output = params.Get<arma::mat>("output");
+  REQUIRE(dataset.n_cols == output.n_cols);
+  REQUIRE(dataset.n_rows == output.n_rows);
+  CheckMatrices(output, dataset);
+}
+
+/**
+ * Test for a dataset with multiple categorical features.
+ */
+TEST_CASE_METHOD(
+    PreprocessOneHotEncodingTestFixture, "MultipleFeatureCategoricalMatrixTest",
+    "[PreprocessOneHotEncodingMainTest][BindingTests]")
+{
+  arma::mat dataset(4, 5);
+  dataset.randu();
+
+  // Dimensions 0, 2, and 3 will be categorical.
+  dataset(0, 0) = 0;
+  dataset(0, 1) = 1;
+  dataset(0, 2) = 2;
+  dataset(0, 3) = 3;
+  dataset(0, 4) = 3;
+  dataset(2, 0) = 0;
+  dataset(2, 1) = 1;
+  dataset(2, 2) = 1;
+  dataset(2, 3) = 2;
+  dataset(2, 4) = 0;
+  dataset(3, 0) = 0;
+  dataset(3, 1) = 0;
+  dataset(3, 2) = 1;
+  dataset(3, 3) = 1;
+  dataset(3, 4) = 1;
+
+  data::DatasetInfo info(4);
+  info.Type(0) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 0);
+  (void) info.MapString<double>("1", 0);
+  (void) info.MapString<double>("2", 0);
+  (void) info.MapString<double>("3", 0);
+
+  info.Type(2) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 2);
+  (void) info.MapString<double>("1", 2);
+  (void) info.MapString<double>("2", 2);
+
+  info.Type(3) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 3);
+  (void) info.MapString<double>("1", 3);
+
+  SetInputParam("input", std::make_tuple(info, dataset));
+  RUN_BINDING();
+
+  arma::mat output = params.Get<arma::mat>("output");
+  REQUIRE(dataset.n_cols == output.n_cols);
+  REQUIRE(dataset.n_rows + 3 + 2 + 1 == output.n_rows);
+
+  // Make sure one-hot encoding was correct.
+  REQUIRE(output(0, 0) == 1);
+  REQUIRE(output(0, 1) == 0);
+  REQUIRE(output(0, 2) == 0);
+  REQUIRE(output(0, 3) == 0);
+  REQUIRE(output(0, 4) == 0);
+  REQUIRE(output(1, 0) == 0);
+  REQUIRE(output(1, 1) == 1);
+  REQUIRE(output(1, 2) == 0);
+  REQUIRE(output(1, 3) == 0);
+  REQUIRE(output(1, 4) == 0);
+  REQUIRE(output(2, 0) == 0);
+  REQUIRE(output(2, 1) == 0);
+  REQUIRE(output(2, 2) == 1);
+  REQUIRE(output(2, 3) == 0);
+  REQUIRE(output(2, 4) == 0);
+  REQUIRE(output(3, 0) == 0);
+  REQUIRE(output(3, 1) == 0);
+  REQUIRE(output(3, 2) == 0);
+  REQUIRE(output(3, 3) == 1);
+  REQUIRE(output(3, 4) == 1);
+
+  REQUIRE(output(5, 0) == 1);
+  REQUIRE(output(5, 1) == 0);
+  REQUIRE(output(5, 2) == 0);
+  REQUIRE(output(5, 3) == 0);
+  REQUIRE(output(5, 4) == 1);
+  REQUIRE(output(6, 0) == 0);
+  REQUIRE(output(6, 1) == 1);
+  REQUIRE(output(6, 2) == 1);
+  REQUIRE(output(6, 3) == 0);
+  REQUIRE(output(6, 4) == 0);
+  REQUIRE(output(7, 0) == 0);
+  REQUIRE(output(7, 1) == 0);
+  REQUIRE(output(7, 2) == 0);
+  REQUIRE(output(7, 3) == 1);
+  REQUIRE(output(7, 4) == 0);
+
+  REQUIRE(output(8, 0) == 1);
+  REQUIRE(output(8, 1) == 1);
+  REQUIRE(output(8, 2) == 0);
+  REQUIRE(output(8, 3) == 0);
+  REQUIRE(output(8, 4) == 0);
+  REQUIRE(output(9, 0) == 0);
+  REQUIRE(output(9, 1) == 0);
+  REQUIRE(output(9, 2) == 1);
+  REQUIRE(output(9, 3) == 1);
+  REQUIRE(output(9, 4) == 1);
+}
+
+/**
+ * Test for a dataset with multiple categorical features, where we are not
+ * converting them all.
+ */
+TEST_CASE_METHOD(
+    PreprocessOneHotEncodingTestFixture,
+    "MultipleNotAllFeatureCategoricalMatrixTest",
+    "[PreprocessOneHotEncodingMainTest][BindingTests]")
+{
+  arma::mat dataset(4, 5);
+  dataset.randu();
+
+  // Dimensions 0, 2, and 3 will be categorical, but we will only convert
+  // dimensions 0 and 2.
+  dataset(0, 0) = 0;
+  dataset(0, 1) = 1;
+  dataset(0, 2) = 2;
+  dataset(0, 3) = 3;
+  dataset(0, 4) = 3;
+  dataset(2, 0) = 0;
+  dataset(2, 1) = 1;
+  dataset(2, 2) = 1;
+  dataset(2, 3) = 2;
+  dataset(2, 4) = 0;
+  dataset(3, 0) = 0;
+  dataset(3, 1) = 0;
+  dataset(3, 2) = 1;
+  dataset(3, 3) = 1;
+  dataset(3, 4) = 1;
+
+  data::DatasetInfo info(4);
+  info.Type(0) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 0);
+  (void) info.MapString<double>("1", 0);
+  (void) info.MapString<double>("2", 0);
+  (void) info.MapString<double>("3", 0);
+
+  info.Type(2) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 2);
+  (void) info.MapString<double>("1", 2);
+  (void) info.MapString<double>("2", 2);
+
+  info.Type(3) = data::Datatype::categorical;
+  (void) info.MapString<double>("0", 3);
+  (void) info.MapString<double>("1", 3);
+
+  SetInputParam("input", std::make_tuple(info, dataset));
+  SetInputParam<vector<int>>("dimensions", {0, 2});
+  RUN_BINDING();
+
+  arma::mat output = params.Get<arma::mat>("output");
+  REQUIRE(dataset.n_cols == output.n_cols);
+  REQUIRE(dataset.n_rows + 3 + 2 == output.n_rows);
+
+  // Make sure one-hot encoding was correct.
+  REQUIRE(output(0, 0) == 1);
+  REQUIRE(output(0, 1) == 0);
+  REQUIRE(output(0, 2) == 0);
+  REQUIRE(output(0, 3) == 0);
+  REQUIRE(output(0, 4) == 0);
+  REQUIRE(output(1, 0) == 0);
+  REQUIRE(output(1, 1) == 1);
+  REQUIRE(output(1, 2) == 0);
+  REQUIRE(output(1, 3) == 0);
+  REQUIRE(output(1, 4) == 0);
+  REQUIRE(output(2, 0) == 0);
+  REQUIRE(output(2, 1) == 0);
+  REQUIRE(output(2, 2) == 1);
+  REQUIRE(output(2, 3) == 0);
+  REQUIRE(output(2, 4) == 0);
+  REQUIRE(output(3, 0) == 0);
+  REQUIRE(output(3, 1) == 0);
+  REQUIRE(output(3, 2) == 0);
+  REQUIRE(output(3, 3) == 1);
+  REQUIRE(output(3, 4) == 1);
+
+  REQUIRE(output(5, 0) == 1);
+  REQUIRE(output(5, 1) == 0);
+  REQUIRE(output(5, 2) == 0);
+  REQUIRE(output(5, 3) == 0);
+  REQUIRE(output(5, 4) == 1);
+  REQUIRE(output(6, 0) == 0);
+  REQUIRE(output(6, 1) == 1);
+  REQUIRE(output(6, 2) == 1);
+  REQUIRE(output(6, 3) == 0);
+  REQUIRE(output(6, 4) == 0);
+  REQUIRE(output(7, 0) == 0);
+  REQUIRE(output(7, 1) == 0);
+  REQUIRE(output(7, 2) == 0);
+  REQUIRE(output(7, 3) == 1);
+  REQUIRE(output(7, 4) == 0);
+
+  // Make sure we did not one-hot encode the last dimension.
+  REQUIRE(output(8, 0) == 0);
+  REQUIRE(output(8, 1) == 0);
+  REQUIRE(output(8, 2) == 1);
+  REQUIRE(output(8, 3) == 1);
+  REQUIRE(output(8, 4) == 1);
+}
diff --git a/src/mlpack/tests/main_tests/softmax_regression_test.cpp b/src/mlpack/tests/main_tests/softmax_regression_test.cpp
index e27f7bf175..d330ccc722 100644
--- a/src/mlpack/tests/main_tests/softmax_regression_test.cpp
+++ b/src/mlpack/tests/main_tests/softmax_regression_test.cpp
@@ -31,7 +31,7 @@ BINDING_TEST_FIXTURE(SoftmaxRegressionTestFixture);
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionOutputDimensionTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -76,7 +76,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionLabelsLessDimensionTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -94,7 +94,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionModelReuseTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -157,7 +157,7 @@ TEST_CASE_METHOD(
  */
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
-    "SoftmaxRegressionMaxItrTest", "[SoftmaxRegressionMainTest][BindingsTests]")
+    "SoftmaxRegressionMaxItrTest", "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -184,7 +184,7 @@ TEST_CASE_METHOD(
  */
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
-    "SoftmaxRegressionLambdaTest", "[SoftmaxRegressionMainTest][BindingsTests]")
+    "SoftmaxRegressionLambdaTest", "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -212,7 +212,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionNumClassesTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -240,7 +240,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionTrainingVerTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   arma::mat inputData;
   if (!data::Load("trainSet.csv", inputData))
@@ -274,7 +274,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionDiffLambdaTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   // Train SR for lambda 0.1.
   arma::mat inputData;
@@ -340,7 +340,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionDiffMaxItrTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   // Train SR for lambda 0.1.
   arma::mat inputData;
@@ -406,7 +406,7 @@ TEST_CASE_METHOD(
 TEST_CASE_METHOD(
     SoftmaxRegressionTestFixture,
     "SoftmaxRegressionDiffInterceptTest",
-    "[SoftmaxRegressionMainTest][BindingsTests]")
+    "[SoftmaxRegressionMainTest][BindingTests]")
 {
   // Train SR with intercept.
   arma::mat inputData;