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Quantile

Quantile loss.

Parameters

  • alpha – defaults to 0.5

    Desired quantile to attain.

Examples

>>> from river import optim

>>> loss = optim.losses.Quantile(0.5)
>>> loss(1, 3)
1.0

>>> loss.gradient(1, 3)
0.5

>>> loss.gradient(3, 1)
-0.5

Methods

call

Returns the loss.

Parameters

  • y_true
  • y_pred

Returns

The loss(es).

gradient

Return the gradient with respect to y_pred.

Parameters

  • y_true
  • y_pred

Returns

The gradient(s).

mean_func

Mean function.

This is the inverse of the link function. Typically, a loss function takes as input the raw output of a model. In the case of classification, the raw output would be logits. The mean function can be used to convert the raw output into a value that makes sense to the user, such as a probability.

Parameters

  • y_pred

Returns

The adjusted prediction(s).

References