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 error. More...

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

Detailed Description

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.

Definition at line 46 of file r2_score.hpp.

Member Function Documentation

◆ Evaluate()

static double Evaluate	(	MLAlgorithm &	model,
		const DataType &	data,
		const ResponsesType &	responses
	)

static

Run prediction and calculate the 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

const bool NeedsMinimization = false

static

Information for hyper-parameter tuning code.

It indicates that we want to maximize the measurement.

Definition at line 67 of file r2_score.hpp.

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

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

Static Public Member Functions

Static Public Attributes

Detailed Description

Member Function Documentation

◆ Evaluate()

Member Data Documentation

◆ NeedsMinimization