mlpack::cv::MSE Class Reference

The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items. More...

#include <mse.hpp>

Static Public Member Functions
template<typename MLAlgorithm , typename DataType , typename ResponsesType >
static double	Evaluate (MLAlgorithm &model, const DataType &data, const ResponsesType &responses)
	Run prediction and calculate the mean squared error. More...

Static Public Attributes
static const bool	NeedsMinimization = true
	Information for hyper-parameter tuning code. More...

Detailed Description

The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items.

Member Function Documentation

Evaluate()

template<typename MLAlgorithm , typename DataType , typename ResponsesType >

double mlpack::cv::MSE::Evaluate ( MLAlgorithm &  model, const DataType &  data, const ResponsesType &  responses  )

static

Run prediction and calculate the mean squared error.

Parameters

model	A regression model.
data	Column-major data containing test items.
responses	Ground truth (correct) target values for the test items, should be either a row vector or a column-major matrix.

Member Data Documentation

NeedsMinimization

const bool mlpack::cv::MSE::NeedsMinimization = true

static

Information for hyper-parameter tuning code.

It indicates that we want to minimize the measurement.

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The documentation for this class was generated from the following files:

• src/mlpack/core/cv/metrics/mse.hpp
• src/mlpack/core/cv/metrics/mse_impl.hpp