diff --git a/src/mlpack/methods/decision_tree/fitness_functions/sse_gain.hpp b/src/mlpack/methods/xgboost/loss_functions/sse_loss.hpp
similarity index 81%
rename from src/mlpack/methods/decision_tree/fitness_functions/sse_gain.hpp
rename to src/mlpack/methods/xgboost/loss_functions/sse_loss.hpp
index b369128aaa..1029fa06af 100644
--- a/src/mlpack/methods/decision_tree/fitness_functions/sse_gain.hpp
+++ b/src/mlpack/methods/xgboost/loss_functions/sse_loss.hpp
@@ -1,8 +1,8 @@
 /**
- * @file methods/decision_tree/gain_functions/sse_gain.hpp
+ * @file methods/xgboost/loss_functions/sse_loss.hpp
  * @author Rishabh Garg
  *
- * The sum of squared error loss class, which is a loss function for gradient
+ * The sum of squared error loss class, which is a loss funtion for gradient
  * xgboost based decision trees.
  *
  * mlpack is free software; you may redistribute it and/or modify it under the
@@ -10,8 +10,8 @@
  * 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_SSE_GAIN_HPP
-#define MLPACK_METHODS_DECISION_TREE_SSE_GAIN_HPP
+#ifndef MLPACK_METHODS_XGBOOST_LOSS_FUNCTIONS_SSE_LOSS_HPP
+#define MLPACK_METHODS_XGBOOST_LOSS_FUNCTIONS_SSE_LOSS_HPP
 
 #include <mlpack/prereqs.hpp>
 
@@ -25,13 +25,13 @@ namespace mlpack {
  *
  * Loss = 1 / 2 * (Observed - Predicted)^2
  */
-class SSEGain
+class SSELoss
 {
  public:
   // Default constructor---No regularization.
-  SSEGain() : alpha(0), lambda(0) { /* Nothing to do. */}
+  SSELoss() : alpha(0), lambda(0) { /* Nothing to do. */}
 
-  SSEGain(const double alpha, const double lambda):
+  SSELoss(const double alpha, const double lambda):
       alpha(alpha), lambda(lambda)
   {
     // Nothing to do.
@@ -66,15 +66,8 @@ class SSEGain
    * @param begin The begin index to calculate gain.
    * @param end The end index to calculate gain.
    */
-  template<bool UseWeights, typename MatType, typename WeightVecType>
-  double Evaluate(const MatType& input, 
-                  const WeightVecType& /* weights */,
-                  const size_t begin, 
-                  const size_t end)
+  double Evaluate(const size_t begin, const size_t end)
   {
-    gradients = (input.row(1) - input.row(0)).t();
-    hessians = arma::vec(input.n_cols, arma::fill::ones);
-
     return std::pow(ApplyL1(accu(gradients.subvec(begin, end))), 2) /
         (accu(hessians.subvec(begin, end)) + lambda);
   }