sklearn.metrics.mean_gamma_deviance
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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
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y_truearray-like of shape (n_samples,)
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Ground truth (correct) target values. Requires y_true > 0.
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y_predarray-like of shape (n_samples,)
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Estimated target values. Requires y_pred > 0.
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sample_weightarray-like of shape (n_samples,), default=None
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Sample weights.
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- Returns
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lossfloat
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A non-negative floating point value (the best value is 0.0).
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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) 1.0568...
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Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.metrics.mean_gamma_deviance.html