Hinge

Computes the hinge loss.

Mathematically, it is defined as

$$L=max(0,1-p_i\ast y_i)$$

Its gradient w.r.t. to $p_i$ is

$$\text{Misplaced \&}$$

Parameters

• threshold

  Default → 1.0

  Margin threshold. 1 yield the loss used in SVMs, whilst 0 is equivalent to the loss used in the Perceptron algorithm.

Examples

from river import optim

loss = optim.losses.Hinge(threshold=1)
loss(1, .2)

0.8

loss.gradient(1, .2)

-1

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).