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

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

model (river.base.estimator.Estimator)

References¶

Coefficient of determination (Wikipedia) ↩