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scikit-learn 1.3.2
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# `sklearn.metrics`.mean_tweedie_deviance

sklearn.metrics.mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)

  Mean Tweedie deviance regression loss.

  Read more in the User Guide.

  Parameters:
    y_true array-like of shape (n_samples,)

      Ground truth (correct) target values.

    y_pred array-like of shape (n_samples,)

      Estimated target values.

    sample_weight array-like of shape (n_samples,), default=None

      Sample weights.

    power float, 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:
    loss float

      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...

## Examples using `sklearn.metrics.mean_tweedie_deviance`

Tweedie regression on insurance claims

Tweedie regression on insurance claims