mean_gamma_deviance#

sklearn.metrics.mean_gamma_deviance(y_true, y_pred, *, sample_weight=None)[source]#

Mean Gamma deviance regression loss.

Gamma deviance is equivalent to the Tweedie deviance with the power parameter power=2. It is invariant to scaling of the target variable, and measures relative errors.

Read more in the User Guide.

Parameters:

y_truearray-like of shape (n_samples,): Ground truth (correct) target values. Requires y_true > 0.
y_predarray-like of shape (n_samples,): Estimated target values. Requires y_pred > 0.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

lossfloat: A non-negative floating point value (the best value is 0.0).

Examples

>>> from sklearn.metrics import mean_gamma_deviance
>>> y_true = [2, 0.5, 1, 4]
>>> y_pred = [0.5, 0.5, 2., 2.]
>>> mean_gamma_deviance(y_true, y_pred)
np.float64(1.0568...)