The correct answer is Coefficient of determination.

Coefficient of determination is the statistic most commonly called R squared. It expresses the proportion of variance in the target variable that is explained by the model and is the standard definition used in statistics and machine learning.

The coefficient of determination is computed as one minus the ratio of the residual sum of squares to the total sum of squares. That formulation makes clear that the metric compares model error to the error of predicting the mean and that higher values indicate better explanatory power up to a maximum of one.

Explained variance score is related because it also measures explained variability but it is not the canonical term R squared. Differences in implementation and handling of bias or multioutput targets can cause the two metrics to differ so the standard name remains the coefficient of determination.

Relative error denotes an error expressed relative to the true value and it is not a measure of explained variance so it is not R squared.

Relative squared error is a normalized squared error metric that compares model squared error to a baseline and it is not the coefficient of determination and therefore it is not called R squared.

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