Misleading parameters in docs for `confustion_matrix()` function

### Describe the issue linked to the documentation

In the documentation for the `confustion_matrix()` function, classification metrics module (see [here][confusion_matrix_docs]), it gives this example:

```py
>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
>>> (tn, fp, fn, tp)
(0, 2, 1, 1)
```

However, this is not actually accurate.

The docs even point to the Wikipedia page. And, as you can see in the below image, it's given the four images in the matrix (from top-left to bottom-right) the following order:
1. True Positive (TP)
1. False Negative (FN)
1. False Positive (FP)
1. True Negative (TN)

![image][wikipedia-image]

And when we generate the confusion matrix (for the given example above), it looks like this:

```py
>>> cm = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0])
>>> print(cm)
[[0 2]
 [1 1]]
>>> print(cm.ravel())
[0 2 1 1]
```

Which means that this documentation should _actually_ give this example like this:

```py
>>> tp, fn, fp, tn = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
>>> (tp, fn, fp, tn)
(0, 2, 1, 1)
```

### Suggest a potential alternative/fix

I'd suggest making the following changes:

1. In the Classification metrics module (file: [scikit-learn/sklearn/metrics/_classification.py][module-classification_metrics], lines: [321-322][lines-confusion_matrix]):
    1. Change from:
        ```py
        >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
        >>> (tn, fp, fn, tp)
        ```
    1. Change to:
        ```py
        >>> tp, fn, fp, tn = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
        >>> (tp, fn, fp, tn)
        ```
1. In the same classification metrics module (line: [1924][lines-class_likelihood_ratios])
    1. Change from:
        ```py
        tn, fp, fn, tp = cm.ravel()
        ```
    1. Change to:
        ```py
        tp, fn, fp, tn = cm.ravel()
        ```
1. In the model evaluation docs (file: [scikit-learn/doc/modules/model_evaluation.rst][module-model_evaluation], lines: [643-644][lines-model_evaluation]):
    1. Change from:
        ```py
        >>> tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
        >>> tn, fp, fn, tp
        ```
    1. Change to:
        ```py
        >>> tp, fn, fp, tn = confusion_matrix(y_true, y_pred).ravel()
        >>> tp, fn, fp, tn
        ```
1. In the supervised cluster metrics module (file: [scikit-learn/sklearn/metrics/cluster/_supervised.py][module-supervised_cluster_metrics], lines: [431-433][lines-supervised_cluster_metrics]):
    1. Change from:
        ```py
        (tn, fp), (fn, tp) = pair_confusion_matrix(labels_true, labels_pred)
        # convert to Python integer types, to avoid overflow or underflow
        tn, fp, fn, tp = int(tn), int(fp), int(fn), int(tp)
        ```
    1. Change to: 
        ```py
        (tp, fn), (fp, tn) = pair_confusion_matrix(labels_true, labels_pred)
        # convert to Python integer types, to avoid overflow or underflow
        tp, fn, fp, tn = int(tp), int(fn), int(fp), int(tn)
        ```

[wikipedia-image]: https://github.com/scikit-learn/scikit-learn/assets/44449504/224be05b-59c7-4831-846d-8b7e680016ed
[confusion_matrix_docs]: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
[confusion_matrix_example]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py#L321-L323
[module-classification_metrics]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py
[lines-confusion_matrix]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py#L321-L322
[lines-class_likelihood_ratios]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py#L1924
[module-model_evaluation]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/doc/modules/model_evaluation.rst
[lines-model_evaluation]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/doc/modules/model_evaluation.rst#L643-L644
[module-supervised_cluster_metrics]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/cluster/_supervised.py
[lines-supervised_cluster_metrics]: https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/cluster/_supervised.py#L431-L433

COMMENT:
Thank you for opening the issue. Scikit-learn does not follow the axes direction with Wikipedia, which we state here:

https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/doc/modules/model_evaluation.rst?plain=1#L601-L602

Looking at the example and computing the confusion matrix manually:

```python
cm = confusion_matrix(
    [0, 1, 0, 1],  # y_true
    [1, 1, 1, 0]   # y_pred
)
```

There is 0 true negative, 2 false positives, 1 false negative, and 1 true positive. This is consistent with the result in the code:

https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py#L321-L323

Furthemore, the docstring for `confusion_matrix` writes explicitly writes out each location:

https://github.com/scikit-learn/scikit-learn/blob/42d235924efa64987a19e945035c85414c53d4f0/sklearn/metrics/_classification.py#L249-L251

Labels: Documentation, Needs Triage