diff --git a/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_regressor_impl.hpp
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+/**
+ * @file methods/decision_tree/decision_tree_regressor_impl.hpp
+ * @author Rishabh Garg
+ *
+ * Implementation of decision tree regressor class.
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
+#define MLPACK_METHODS_DECISION_TREE_DECISION_TREE_REGRESSOR_IMPL_HPP
+
+#include "decision_tree_regressor.hpp"
+
+namespace mlpack {
+namespace tree {
+
+//! Construct, don't train.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor() :
+    splitDimension(0),
+    dimensionTypeOrMajorityClass(0),
+    classProbabilities(numClasses)
+{
+  // Initialize utility vector.
+  classProbabilities.fill(1.0 / (double) numClasses);
+}
+
+//! Construct and train without weight.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  Train<false>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+      weights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Construct and train without weight on numeric data.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    LabelsType labels,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the Train() method.
+  arma::rowvec weights; // Fake weights, not used.
+  Train<false>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, weights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Construct and train with weights.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<WeightsType>::type>::value>*)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+      tmpWeights, minimumLeafSize, minimumGainSplit, maximumDepth,
+      dimensionSelector);
+}
+
+//! Construct and train on numeric data with weights.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<
+        arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Take ownership of another tree and train with weights.
+template<typename FitnessFunction,
+        template<typename> class NumericSplitType,
+        template<typename> class CategoricalSplitType,
+        typename DimensionSelectionType,
+        bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other,
+    MatType data,
+    const data::DatasetInfo& datasetInfo,
+    LabelsType labels,
+    const size_t numClasses,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<WeightsType>::type>::value>*):
+        NumericAuxiliarySplitInfo(other),
+        CategoricalAuxiliarySplitInfo(other)
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
+              tmpWeights, minimumLeafSize, minimumGainSplit);
+}
+
+//! Take ownership of another tree and train with weights.
+template<typename FitnessFunction,
+        template<typename> class NumericSplitType,
+        template<typename> class CategoricalSplitType,
+        typename DimensionSelectionType,
+        bool NoRecursion>
+template<typename MatType, typename LabelsType, typename WeightsType>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other,
+    MatType data,
+    LabelsType labels,
+    WeightsType weights,
+    const size_t minimumLeafSize,
+    const double minimumGainSplit,
+    const size_t maximumDepth,
+    DimensionSelectionType dimensionSelector,
+    const std::enable_if_t<arma::is_arma_type<
+        typename std::remove_reference<
+        WeightsType>::type>::value>*):
+        NumericAuxiliarySplitInfo(other),
+        CategoricalAuxiliarySplitInfo(other)  // other info does need to copy
+{
+  using TrueMatType = typename std::decay<MatType>::type;
+  using TrueLabelsType = typename std::decay<LabelsType>::type;
+  using TrueWeightsType = typename std::decay<WeightsType>::type;
+
+  // Copy or move data.
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
+
+  // Set the correct dimensionality for the dimension selector.
+  dimensionSelector.Dimensions() = tmpData.n_rows;
+
+  // Pass off work to the weighted Train() method.
+  Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
+      minimumLeafSize, minimumGainSplit, maximumDepth, dimensionSelector);
+}
+
+//! Copy another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion
+>::DecisionTreeRegressor(
+    const DecisionTreeRegressor& other) :
+    NumericAuxiliarySplitInfo(other),
+    CategoricalAuxiliarySplitInfo(other),
+    splitDimension(other.splitDimension),
+    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
+    classProbabilities(other.classProbabilities)
+{
+  // Copy each child.
+  for (size_t i = 0; i < other.children.size(); ++i)
+    children.push_back(new DecisionTreeRegressor(*other.children[i]));
+}
+
+//! Take ownership of another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion
+>::DecisionTreeRegressor(
+    DecisionTreeRegressor&& other) :
+    NumericAuxiliarySplitInfo(std::move(other)),
+    CategoricalAuxiliarySplitInfo(std::move(other)),
+    children(std::move(other.children)),
+    splitDimension(other.splitDimension),
+    dimensionTypeOrMajorityClass(other.dimensionTypeOrMajorityClass),
+    classProbabilities(std::move(other.classProbabilities))
+{
+  // Reset the other object.
+  other.classProbabilities.ones(1); // One class, P(1) = 1.
+}
+
+//! Copy another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>&
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion
+>::operator=(const DecisionTreeRegressor& other)
+{
+  if (this == &other)
+    return *this; // Nothing to copy.
+
+  // Clean memory if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // Copy everything from the other tree.
+  splitDimension = other.splitDimension;
+  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
+  classProbabilities = other.classProbabilities;
+
+  // Copy the children.
+  for (size_t i = 0; i < other.children.size(); ++i)
+    children.push_back(new DecisionTree(*other.children[i]));
+
+  // Copy the auxiliary info.
+  NumericAuxiliarySplitInfo::operator=(other);
+  CategoricalAuxiliarySplitInfo::operator=(other);
+
+  return *this;
+}
+
+//! Take ownership of another tree.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion>&
+DecisionTreeRegressor<FitnessFunction,
+                      NumericSplitType,
+                      CategoricalSplitType,
+                      DimensionSelectionType,
+                      NoRecursion
+>::operator=(DecisionTreeRegressor&& other)
+{
+  if (this == &other)
+    return *this; // Nothing to move.
+
+  // Clean memory if needed.
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+  children.clear();
+
+  // Take ownership of the other tree's components.
+  children = std::move(other.children);
+  splitDimension = other.splitDimension;
+  dimensionTypeOrMajorityClass = other.dimensionTypeOrMajorityClass;
+  classProbabilities = std::move(other.classProbabilities);
+
+  // Reset the class probabilities of the other object.
+  other.classProbabilities.ones(1); // One class, P(1) = 1.
+
+  // Take ownership of the auxiliary info.
+  NumericAuxiliarySplitInfo::operator=(std::move(other));
+  CategoricalAuxiliarySplitInfo::operator=(std::move(other));
+
+  return *this;
+}
+
+//! Clean up memory.
+template<typename FitnessFunction,
+         template<typename> class NumericSplitType,
+         template<typename> class CategoricalSplitType,
+         typename DimensionSelectionType,
+         bool NoRecursion>
+DecisionTreeRegressor<FitnessFunction,
+             NumericSplitType,
+             CategoricalSplitType,
+             DimensionSelectionType,
+             NoRecursion>::~DecisionTreeRegressor()
+{
+  for (size_t i = 0; i < children.size(); ++i)
+    delete children[i];
+}
+
+
+} // namespace tree
+} // namespace mlpack
+
+#endif