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...

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.

Definition at line 25 of file mse.hpp.

Member Function Documentation

Evaluate()

static double 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 NeedsMinimization = true

static

Information for hyper-parameter tuning code.

It indicates that we want to minimize the measurement.

Definition at line 45 of file mse.hpp.

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

• /home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/core/cv/metrics/mse.hpp