diff --git a/src/mlpack/methods/logistic_regression/logistic_regression_function.hpp b/src/mlpack/methods/logistic_regression/logistic_regression_function.hpp
index e531930901..6b8a6a3175 100644
--- a/src/mlpack/methods/logistic_regression/logistic_regression_function.hpp
+++ b/src/mlpack/methods/logistic_regression/logistic_regression_function.hpp
@@ -28,10 +28,25 @@ template<typename MatType = arma::mat>
 class LogisticRegressionFunction
 {
  public:
+  /**
+   * Creates the LogisticRegressionFunction.
+   *
+   * @param predictors The matrix of data points.
+   * @param responses The measured data for each point in predictors.
+   * @param lambda Regularization constant for ridge regression.
+   */
   LogisticRegressionFunction(const MatType& predictors,
                              const arma::Row<size_t>& responses,
                              const double lambda = 0);
 
+  /**
+   * Creates the LogisticRegressionFunction with initialPoint.
+   *
+   * @param predictors The matrix of data points.
+   * @param responses The measured data for each point in predictors.
+   * @param initialPoint Point from which to start the optimization.
+   * @param lambda Regularization constant for ridge regression.
+   */
   LogisticRegressionFunction(const MatType& predictors,
                              const arma::Row<size_t>& responses,
                              const arma::vec& initialPoint,
@@ -59,7 +74,7 @@ class LogisticRegressionFunction
 
   /**
    * Evaluate the logistic regression log-likelihood function with the given
-   * parameters.  Note that if a point has 0 probability of being classified
+   * parameters. Note that if a point has 0 probability of being classified
    * directly with the given parameters, then Evaluate() will return nan (this
    * is kind of a corner case and should not happen for reasonable models).
    *
@@ -72,9 +87,9 @@ class LogisticRegressionFunction
 
   /**
    * Evaluate the logistic regression log-likelihood function with the given
-   * parameters using the given batch size from the given point index.  This is
+   * parameters using the given batch size from the given point index. This is
    * useful for optimizers such as SGD, which require a separable objective
-   * function.  Note that if the points have 0 probability of being classified
+   * function. Note that if the points have 0 probability of being classified
    * correctly with the given parameters, then Evaluate() will return nan (this
    * is kind of a corner case and should not happen for reasonable models).
    *
@@ -102,8 +117,8 @@ class LogisticRegressionFunction
 
   /**
    * Evaluate the gradient of the logistic regression log-likelihood function
-   * with the given parameters, for the given batch size from a given point the
-   * in dataset. This is useful for optimizers such as SGD, which require a
+   * with the given parameters, for the given batch size from a given point in
+   * the dataset. This is useful for optimizers such as SGD, which require a
    * separable objective function.
    *
    * @param parameters Vector of logistic regression parameters.
@@ -122,7 +137,7 @@ class LogisticRegressionFunction
   /**
    * Evaluate the gradient of the logistic regression log-likelihood function
    * with the given parameters, and with respect to only one feature in the
-   * dataset.  This is useful for optimizers such as SCD, which require
+   * dataset. This is useful for optimizers such as SCD, which require
    * partial gradients.
    *
    * @param parameters Vector of logistic regression parameters.
@@ -142,6 +157,11 @@ class LogisticRegressionFunction
   double EvaluateWithGradient(const arma::mat& parameters,
                               GradType& gradient) const;
 
+  /**
+   * Evaluate the objective function and gradient of the logistic regression
+   * log-likelihood function simultaneously with the given parameters, for
+   * the given batch size from a given point in the dataset.
+   */
   template<typename GradType>
   double EvaluateWithGradient(const arma::mat& parameters,
                               const size_t begin,