mse.hpp
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1 
12 #ifndef MLPACK_CORE_CV_METRICS_MSE_HPP
13 #define MLPACK_CORE_CV_METRICS_MSE_HPP
14 
15 #include <mlpack/core.hpp>
16 
17 namespace mlpack {
18 namespace cv {
19 
25 class MSE
26 {
27  public:
36  template<typename MLAlgorithm, typename DataType, typename ResponsesType>
37  static double Evaluate(MLAlgorithm& model,
38  const DataType& data,
39  const ResponsesType& responses);
40 
45  static const bool NeedsMinimization = true;
46 };
47 
48 } // namespace cv
49 } // namespace mlpack
50 
51 // Include implementation.
52 #include "mse_impl.hpp"
53 
54 #endif
The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean s...
Definition: mse.hpp:25
Linear algebra utility functions, generally performed on matrices or vectors.
constexpr auto data(Container const &container) noexcept -> decltype(container.data())
Definition: iterator.hpp:79
Include all of the base components required to write mlpack methods, and the main mlpack Doxygen docu...
static double Evaluate(MLAlgorithm &model, const DataType &data, const ResponsesType &responses)
Run prediction and calculate the mean squared error.
static const bool NeedsMinimization
Information for hyper-parameter tuning code.
Definition: mse.hpp:45