mean_tweedie_deviance (original) (raw)
sklearn.metrics.mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)[source]#
Mean Tweedie deviance regression loss.
Read more in the User Guide.
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 mean_squared_error. 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:
lossfloat
A non-negative floating point value (the best value is 0.0).
Examples
from sklearn.metrics import mean_tweedie_deviance y_true = [2, 0, 1, 4] y_pred = [0.5, 0.5, 2., 2.] mean_tweedie_deviance(y_true, y_pred, power=1) 1.4260...