# 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¶

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

• y_true (numbers.Number)
• y_pred (numbers.Number)
• sample_weight (numbers.Number)
update

Update the metric.

Parameters

• y_true (numbers.Number)
• y_pred (numbers.Number)
• sample_weight (numbers.Number) – defaults to 1.0
works_with

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

Parameters

• model (river.base.estimator.Estimator)