# CrossEntropy

Cross entropy loss.

This is a generalization of logistic loss to multiple classes.

## Parameters

- **class\_weight**

  *Type* → `dict[base.typing.ClfTarget, float] | None`

  *Default* → `None`

  A dictionary that indicates what weight to associate with each class.

## Examples

```python
from river import optim

y_true = [0, 1, 2, 2]
y_pred = [
    {0: 0.29450637, 1: 0.34216758, 2: 0.36332605},
    {0: 0.21290077, 1: 0.32728332, 2: 0.45981591},
    {0: 0.42860913, 1: 0.33380113, 2: 0.23758974},
    {0: 0.44941979, 1: 0.32962558, 2: 0.22095463}
]

loss = optim.losses.CrossEntropy()

for yt, yp in zip(y_true, y_pred):
    print(loss(yt, yp))
```

```text
1.222454
1.116929
1.437209
1.509797
```

```python
for yt, yp in zip(y_true, y_pred):
    print(loss.gradient(yt, yp))
```

```text
{0: -0.70549363, 1: 0.34216758, 2: 0.36332605}
{0: 0.21290077, 1: -0.67271668, 2: 0.45981591}
{0: 0.42860913, 1: 0.33380113, 2: -0.76241026}
{0: 0.44941979, 1: 0.32962558, 2: -0.77904537}
```

## Methods

> [!NOTE]
>
> ****call****
>
> Returns the loss.
>
> **Parameters**
>
> - **y\_true** — `typing.Any`
> - **y\_pred** — `typing.Any`
>
> **Returns**
>
> `typing.Any`: The loss(es).

> [!NOTE]
>
> **gradient**
>
> Return the gradient with respect to y\_pred.
>
> **Parameters**
>
> - **y\_true** — `typing.Any`
> - **y\_pred** — `typing.Any`
>
> **Returns**
>
> `typing.Any`: The gradient(s).

> [!NOTE]
>
> **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** — `typing.Any`
>
> **Returns**
>
> `typing.Any`: The adjusted prediction(s).

## References

- [What is Softmax regression and how is it related to Logistic regression?](<https://github.com/rasbt/python-machine-learning-book/blob/master/faq/softmax_regression.md>)