The least-squares loss function for SGD algorithms. More...

Inheritance diagram for meta::classify::loss::least_squares:

Public Member Functions
double	loss (double prediction, int expected) const override
	The loss incurred in assigning the given prediction value, given the correct value of the prediction. More...

double	derivative (double prediction, int expected) const override
	The derivative of the loss function given a predicted value and the expected result of that prediction. More...

Static Public Attributes
static const std::string	id = "least-squares"
	The identifier for this loss function.

Detailed Description

The least-squares loss function for SGD algorithms.

Defined as \(\phi(p, y) = (p - y)^2\).

Member Function Documentation

double meta::classify::loss::least_squares::loss	(	double	prediction,
		int	expected
	)		const

overridevirtual

The loss incurred in assigning the given prediction value, given the correct value of the prediction.

Parameters

prediction	The prediction obtained from the model (dot product)
expected	The expected (as in, correct) value of the model's prediction

double meta::classify::loss::least_squares::derivative	(	double	prediction,
		int	expected
	)		const

overridevirtual

The derivative of the loss function given a predicted value and the expected result of that prediction.

Parameters

prediction	The prediction obtained from the model (dot product)
expected	The expected (as in, correct) value of the model's prediction

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