The R2 Score is a metric of performance for regression algorithms that represents the proportion of variance (here y) that has been explained by the independent variables in the model. More...

#include <r2_score.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 R squared or Adjusted R squared error. More...

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

Detailed Description

template<bool AdjustedR2>
class mlpack::cv::R2Score< AdjustedR2 >

The R2 Score is a metric of performance for regression algorithms that represents the proportion of variance (here y) that has been explained by the independent variables in the model.

It provides an indication of goodness of fit and therefore a measure of how well unseen samples are likely to be predicted by the model, through the proportion of explained variance. As R2 Score is dataset dependent it can have wide range of values. The best possible score is \(R^2 =1.0\). Values of R2 outside the range 0 to 1 can occur when the model fits the data worse than a horizontal hyperplane. This would occur when the wrong model was chosen, or nonsensical constraints were applied by mistake. A model which predicts exactly the expected value of y, disregarding the input features, gets a R2 Score equals to 0.0. If a model predicts \( \hat{y}_i \) of the \( i \)-th sample for a true \( y_i \) for total n samples, the R2 Score is calculated by

\begin{eqnarray*} R^{2} \left( y, \hat{y} \right) &=& 1-\frac{\sum_{i=1}^{n} \left( y_i - \hat{y_i} \right)^2 } {\sum_{i=1}^{n} \left( y_i - \bar{y}\right)^2}\\ \end{eqnarray*}

where \( \bar{y} = frac{1}{y}\sum_{i=1}^{n} y_i \). For example, a model having R2Score = 0.85, explains 85 % variability of the response data around its mean.

Template Parameters

AdjustedR2 If true, then the Adjusted R2 score will be used. Otherwise, the regular R2 score is used.

Member Function Documentation

◆ Evaluate()

template<bool AdjustedR2>

template<typename MLAlgorithm , typename DataType , typename ResponsesType >

double mlpack::cv::R2Score< AdjustedR2 >::Evaluate	(	MLAlgorithm &	model,
		const DataType &	data,
		const ResponsesType &	responses
	)

static

Run prediction and calculate the R squared or Adjusted R 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.

Returns: calculated R2 Score.

Member Data Documentation

◆ NeedsMinimization

template<bool AdjustedR2>

const bool mlpack::cv::R2Score< AdjustedR2 >::NeedsMinimization = false

static

Information for hyper-parameter tuning code.

It indicates that we want to maximize the measurement.

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

src/mlpack/core/cv/metrics/r2_score.hpp
src/mlpack/core/cv/metrics/r2_score_impl.hpp

Static Public Member Functions