Metric that quantifies the difference between predicted values and actual values in terms of mean squared error (MSE). More...

#include <MSE.hpp>

Inheritance diagram for sgpp::datadriven::MSE:

Public Member Functions
Metric *	clone () const override
	Polymorphic clone pattern.

double	measure (const DataVector &predictedValues, const DataVector &trueValues, const ModelFittingBase &model, Dataset &testDataset) const override
	Quantify the difference between predicted values and actual values in terms of mean squared error.

double	measureLowerIsBetter (const DataVector &predictedValues, const DataVector &trueValues, const ModelFittingBase &model, Dataset &testDataset) const override
	Quantify the difference between predicted values and actual values in terms of mean squared error.

Public Member Functions inherited from sgpp::datadriven::Metric
	Metric ()=default
	Default constructor.

	Metric (const Metric &rhs)=default
	Copy constructor.

	Metric (Metric &&rhs)=default
	Move constructor.

Metric &	operator= (const Metric &rhs)=default
	Copy assign operator.

Metric &	operator= (Metric &&rhs)=default
	Move assign operator.

virtual	~Metric ()=default
	virtual destructor.

Detailed Description

Metric that quantifies the difference between predicted values and actual values in terms of mean squared error (MSE).

MSE is defined strictly positive such that smaller values are better.

Member Function Documentation

Metric * sgpp::datadriven::MSE::clone ( ) const

overridevirtual

Polymorphic clone pattern.

double sgpp::datadriven::MSE::measure	(	const DataVector &	predictedValues,
		const DataVector &	trueValues,
		const ModelFittingBase &	model,
		Dataset &	testDataset
	)		const

overridevirtual

Quantify the difference between predicted values and actual values in terms of mean squared error.

Parameters

predictedValues	values calculated by the model for testing data
trueValues	actual values as taken from the dataset.
model	reference to the model
testDataset	dataset with test data

Returns: mean squared error (MSE) - strictly positive such that smaller values are better.

double sgpp::datadriven::MSE::measureLowerIsBetter	(	const DataVector &	predictedValues,
		const DataVector &	trueValues,
		const ModelFittingBase &	model,
		Dataset &	testDataset
	)		const

overridevirtual

Quantify the difference between predicted values and actual values in terms of mean squared error.

Parameters

predictedValues	values calculated by the model for testing data
trueValues	actual values as taken from the dataset.
model	reference to the model
testDataset	dataset with test data

Returns: mean squared error (MSE) - strictly positive such that smaller values are better.

References measure().

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