sklearn.metrics
.d2_tweedie_score¶
- sklearn.metrics.d2_tweedie_score(y_true, y_pred, *, sample_weight=None, power=0)[source]¶
D^2 regression score function, fraction of Tweedie deviance explained.
Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A model that always uses the empirical mean of
y_true
as constant prediction, disregarding the input features, gets a D^2 score of 0.0.Read more in the User Guide.
New in version 1.0.
- Parameters:
- y_truearray-like of shape (n_samples,)
Ground truth (correct) target values.
- y_predarray-like of shape (n_samples,)
Estimated target values.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- powerfloat, default=0
Tweedie power parameter. Either power <= 0 or power >= 1.
The higher
p
the less weight is given to extreme deviations between true and predicted targets.power < 0: Extreme stable distribution. Requires: y_pred > 0.
power = 0 : Normal distribution, output corresponds to r2_score. y_true and y_pred can be any real numbers.
power = 1 : Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0 and y_pred > 0.
power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
power = 3 : Inverse Gaussian distribution. Requires: y_true > 0 and y_pred > 0.
otherwise : Positive stable distribution. Requires: y_true > 0 and y_pred > 0.
- Returns:
- zfloat or ndarray of floats
The D^2 score.
Notes
This is not a symmetric function.
Like R^2, D^2 score may be negative (it need not actually be the square of a quantity D).
This metric is not well-defined for single samples and will return a NaN value if n_samples is less than two.
References
[1]Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J. Wainwright. “Statistical Learning with Sparsity: The Lasso and Generalizations.” (2015). https://hastie.su.domains/StatLearnSparsity/
Examples
>>> from sklearn.metrics import d2_tweedie_score >>> y_true = [0.5, 1, 2.5, 7] >>> y_pred = [1, 1, 5, 3.5] >>> d2_tweedie_score(y_true, y_pred) 0.285... >>> d2_tweedie_score(y_true, y_pred, power=1) 0.487... >>> d2_tweedie_score(y_true, y_pred, power=2) 0.630... >>> d2_tweedie_score(y_true, y_true, power=2) 1.0