mean_tweedie_deviance#

sklearn.metrics.mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0)[source]#

平均 Tweedie 偏差回归损失。

在用户指南中了解更多信息。

参数:
y_true形状为 (n_samples,) 的 array-like

真实（正确）的目标值。

y_pred形状为 (n_samples,) 的类数组

估计的目标值。

sample_weightshape 为 (n_samples,) 的 array-like, default=None

样本权重。

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.

返回:
loss浮点数

一个非负浮点值（最佳值为 0.0）。

示例

>>> 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...