mlpack 3.4.2
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
17namespace mlpack {
18namespace cv {
19
25class 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:26
static const bool NeedsMinimization
Information for hyper-parameter tuning code.
Definition: mse.hpp:45
static double Evaluate(MLAlgorithm &model, const DataType &data, const ResponsesType &responses)
Run prediction and calculate the mean squared error.
Include all of the base components required to write mlpack methods, and the main mlpack Doxygen docu...
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1