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
- model — 'base.Estimator'