# R2¶

Coefficient of determination ($$R^2$$) score

The coefficient of determination, denoted $$R^2$$ or $$r^2$$, is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). 1

Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of $$y$$, disregarding the input features, would get a $$R^2$$ score of 0.0.

$$R^2$$ is not defined when less than 2 samples have been observed. This implementation returns 0.0 in this case.

## Attributes¶

• bigger_is_better

Indicate if a high value is better than a low one or not.

• works_with_weights

Indicate whether the model takes into consideration the effect of sample weights

## Examples¶

from river import metrics

y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]

metric = metrics.R2()

for yt, yp in zip(y_true, y_pred):
print(metric.update(yt, yp).get())

0.0
0.9183
0.9230
0.9486


## Methods¶

get

Return the current value of the metric.

is_better_than

Indicate if the current metric is better than another one.

Parameters

• other

revert

Revert the metric.

Parameters

• y_true'numbers.Number'
• y_pred'numbers.Number'
• sample_weight — defaults to 1.0

update

Update the metric.

Parameters

• y_true'numbers.Number'
• y_pred'numbers.Number'
• sample_weight — defaults to 1.0

works_with

Indicates whether or not a metric can work with a given model.

Parameters