loss: Errors suffered by a sequence of predictions
In opera: Online Prediction by Expert Aggregation

Description Usage Arguments Value Author(s)

The function loss computes the sequence of instantaneous losses suffered by the predictions in x to predict the observation in y.

loss(
  x,
  y,
  pred = NULL,
  loss.type = list(name = "square"),
  loss.gradient = FALSE
)

`x`	`numeric`. A vector of length `T` containing the sequence of prediction to be evaluated.
`y`	`numeric`. A vector of length `T` that contains the observations to be predicted.
`pred`	`numeric`. A vector of length `T` containing the sequence of real values.
`loss.type`	`character, list or function` ("square"). character Name of the loss to be applied ('square', 'absolute', 'percentage', or 'pinball'); list List with field `name` equal to the loss name. If using pinball loss, field `tau` equal to the required quantile in [0,1]; function A custom loss as a function of two parameters.
`loss.gradient`	`boolean, function` (TRUE). boolean If TRUE, the aggregation rule will not be directly applied to the loss function at hand, but to a gradient version of it. The aggregation rule is then similar to gradient descent aggregation rule. function If loss.type is a function, the derivative should be provided to be used (it is not automatically computed).

A vector of length T containing the sequence of instantaneous losses suffered by the expert previsions (x) or the gradient computed on the aggregated previsions (pred).

Pierre Gaillard <pierre@gaillard.me>

opera documentation built on Dec. 11, 2021, 9:07 a.m.