Minor style fixes.

HISTORY.md

@@ -2,7 +2,8 @@
 ###### ????-??-??
   * Add Multiple Pole Balancing Environment (#1901).
 
-  * Add New paramter Maximum_depth to Decision Tree And Random Forest (#1916).
+  * Add new parameter `maximum_depth` to decision tree and random forest
+    bindings (#1916).
 
   * Fix prediction output of softmax regression when test set accuracy is
     calculated (#1922).

src/mlpack/methods/decision_tree/decision_tree_impl.hpp

@@ -657,6 +657,7 @@ double DecisionTree<FitnessFunction,
         break;
     }
   }
+
   // Did we split or not?  If so, then split the data and create the children.
   if (bestDim != datasetInfo.Dimensionality())
   {
@@ -828,6 +829,7 @@ double DecisionTree<FitnessFunction,
         break;
     }
   }
+
   // Did we split or not?  If so, then split the data and create the children.
   if (bestDim != data.n_rows)
   {

src/mlpack/methods/decision_tree/decision_tree_main.cpp

@@ -47,9 +47,9 @@ PROGRAM_INFO("Decision tree",
     " parameter specifies the minimum number of training points that must fall"
     " into each leaf for it to be split.  The " +
     PRINT_PARAM_STRING("minimum_gain_split") + " parameter specifies "
-    "the minimum gain that is needed for the node to split. The " +
+    "the minimum gain that is needed for the node to split.  The " +
     PRINT_PARAM_STRING("maximum_depth") + " parameter specifies "
-    "the maximum depth of the tree. If " +
+    "the maximum depth of the tree.  If " +
     PRINT_PARAM_STRING("print_training_error") + " is specified, the training "
     "error will be printed."
     "\n\n"
@@ -102,7 +102,7 @@ PARAM_INT_IN("minimum_leaf_size", "Minimum number of points in a leaf.", "n",
     20);
 PARAM_DOUBLE_IN("minimum_gain_split", "Minimum gain for node splitting.", "g",
     1e-7);
-PARAM_INT_IN("maximum_depth", "Maximum Depth of the tree.(0 means no limit)",
+PARAM_INT_IN("maximum_depth", "Maximum depth of the tree (0 means no limit).",
     "D", 0);
 // This is deprecated and should be removed in mlpack 4.0.0.
 PARAM_FLAG("print_training_error", "Print the training error (deprecated; will "
@@ -162,7 +162,7 @@ static void mlpackMain()
       "leaf size must be positive");
 
   RequireParamValue<int>("maximum_depth", [](int x) { return x >= 0; }, true,
-      "depth must not be negative");
+      "maximum depth must not be negative");
 
   RequireParamValue<double>("minimum_gain_split", [](double x)
                          { return (x > 0.0 && x < 1.0); }, true,

src/mlpack/methods/random_forest/random_forest_main.cpp

@@ -50,7 +50,7 @@ PROGRAM_INFO("Random forests",
     " controls the number of trees in the random forest.  The " +
     PRINT_PARAM_STRING("minimum_gain_split") + " parameter controls the minimum"
     " required gain for a decision tree node to split.  Larger values will "
-    "force higher-confidence splits. The " +
+    "force higher-confidence splits.  The " +
     PRINT_PARAM_STRING("maximum_depth") + " parameter specifies "
     "the maximum depth of the tree.  The " +
     PRINT_PARAM_STRING("subspace_dim") + " parameter is used to control the "
@@ -107,7 +107,7 @@ PARAM_FLAG("print_training_accuracy", "If set, then the accuracy of the model "
 PARAM_INT_IN("num_trees", "Number of trees in the random forest.", "N", 10);
 PARAM_INT_IN("minimum_leaf_size", "Minimum number of points in each leaf "
     "node.", "n", 1);
-PARAM_INT_IN("maximum_depth", "Maximum depth of the tree.(0 means no limit)",
+PARAM_INT_IN("maximum_depth", "Maximum depth of the tree (0 means no limit).",
     "D", 0);
 PARAM_MATRIX_OUT("probabilities", "Predicted class probabilities for each "
     "point in the test set.", "P");
@@ -181,7 +181,7 @@ static void mlpackMain()
   RequireParamValue<int>("minimum_leaf_size", [](int x) { return x > 0; }, true,
       "minimum leaf size must be greater than 0");
   RequireParamValue<int>("maximum_depth", [](int x) { return x >= 0; }, true,
-      "depth must not be negative");
+      "maximum depth must not be negative");
   RequireParamValue<int>("subspace_dim", [](int x) { return x >= 0; }, true,
       "subspace dimensionality must be nonnegative");
   RequireParamValue<double>("minimum_gain_split",

src/mlpack/tests/decision_tree_test.cpp

@@ -1217,7 +1217,7 @@ BOOST_AUTO_TEST_CASE(DecisionTreeCategoricalTrainReturnEntropy)
 }
 
 /**
- * Make sure different Maximum Depth gives different number of children.
+ * Make sure different maximum depth values give different numbers of children.
  */
 BOOST_AUTO_TEST_CASE(DifferentMaximumDepthTest)
 {

src/mlpack/tests/main_tests/decision_tree_test.cpp

@@ -174,7 +174,7 @@ BOOST_AUTO_TEST_CASE(DecisionTreeMinimumLeafSizeTest)
 }
 
 /**
- * Make sure maximum depth size is always a non-negative number.
+ * Make sure maximum depth is always a non-negative number.
  */
 BOOST_AUTO_TEST_CASE(DecisionTreeNonNegativeMaximumDepthTest)
 {
@@ -201,6 +201,7 @@ BOOST_AUTO_TEST_CASE(DecisionTreeNonNegativeMaximumDepthTest)
   BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
   Log::Fatal.ignoreInput = false;
 }
+
 /**
  * Make sure minimum gain split is always a fraction in range [0,1].
  */
@@ -454,8 +455,7 @@ BOOST_AUTO_TEST_CASE(DecisionModelCategoricalReuseTest)
 }
 
 /**
- * Check that different maximum depth gives
- * different results.
+ * Check that different maximum depths give different results.
  */
 BOOST_AUTO_TEST_CASE(DecisionTreeMaximumDepthTest)
 {