Aakash-kaushik/mlpack/mse_8hpp_source.html

 #ifndef MLPACK_CORE_CV_METRICS_MSE_HPP
 #define MLPACK_CORE_CV_METRICS_MSE_HPP

 #include <mlpack/core.hpp>

 namespace mlpack {
 namespace cv {

 class MSE
 {
  public:
   template<typename MLAlgorithm, typename DataType, typename ResponsesType>
   static double Evaluate(MLAlgorithm& model,
                          const DataType& data,
                          const ResponsesType& responses);

   static const bool NeedsMinimization = true;
 };

 } // namespace cv
 } // namespace mlpack

 // Include implementation.
 #include "mse_impl.hpp"

 #endif
mse_impl.hpp

mlpack::cv::MSE
The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean s...
Definition: mse.hpp:25

mlpack::cv::MSE::NeedsMinimization
static const bool NeedsMinimization
Information for hyper-parameter tuning code.
Definition: mse.hpp:45

data

mlpack
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1

mlpack::cv::MSE::Evaluate
static double Evaluate(MLAlgorithm &model, const DataType &data, const ResponsesType &responses)
Run prediction and calculate the mean squared error.
Definition: mse_impl.hpp:19

core.hpp
Include all of the base components required to write mlpack methods, and the main mlpack Doxygen docu...