Repository: scikit-learn/scikit-learn
File: sklearn/manifold/_mds.py (Python)

Code before:
        computed in parallel.

        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    random_state : int, RandomState instance or None, default=None
        Determines the random number generator used to initialize the centers.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.

    dissimilarity : {'euclidean', 'precomputed'}
        Dissimilarity measure to use:

        - 'euclidean':
            Pairwise Euclidean distances between points in the dataset.

        - 'precomputed':
            Pre-computed dissimilarities are passed directly to ``fit`` and
            ``fit_transform``.

        .. versionchanged:: 1.8
           Deprecated. Use `'metric` instead.

        .. versionchanged:: 1.10
           This parameter will be removed in version 1.10.

    metric : str or callable, default='euclidean'
        Metric to use for dissimilarity computation. Default is "euclidean".

        If metric is a string, it must be one of the options allowed by
        `scipy.spatial.distance.pdist` for its metric parameter, or a metric
        listed in :func:`sklearn.metrics.pairwise.distance_metrics`

        If metric is "precomputed", X is assumed to be a distance matrix and
        must be square during fit.

        If metric is a callable function, it takes two arrays representing 1D
        vectors as inputs and must return one value indicating the distance
        between those vectors. This works for Scipy's metrics, but is less
        efficient than passing the metric name as a string.

        .. versionadded:: 1.8

        .. versionchanged:: 1.10
           The support for ``True`` and ``False`` will be dropped in version
           1.10.

    metric_params : dict, default=None
        Additional keyword arguments for the dissimilarity computation.


Review comment (suggestion):
I'm unsure about the deprecation guidelines, but according to [deprecating a parameter guideline](https://scikit-learn.org/dev/developers/contributing.html#deprecation):
```suggestion
        .. deprecated:: 1.8
            `dissimilarity` was renamed `metric` in 1.8 and will be removed in 1.10.
```

Code after:
        computed in parallel.

        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    random_state : int, RandomState instance or None, default=None
        Determines the random number generator used to initialize the centers.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.

    dissimilarity : {'euclidean', 'precomputed'}
        Dissimilarity measure to use:

        - 'euclidean':
            Pairwise Euclidean distances between points in the dataset.

        - 'precomputed':
            Pre-computed dissimilarities are passed directly to ``fit`` and
            ``fit_transform``.

        .. deprecated:: 1.8
           `dissimilarity` was renamed `metric` in 1.8 and will be removed in 1.10.

    metric : str or callable, default='euclidean'
        Metric to use for dissimilarity computation. Default is "euclidean".

        If metric is a string, it must be one of the options allowed by
        `scipy.spatial.distance.pdist` for its metric parameter, or a metric
        listed in :func:`sklearn.metrics.pairwise.distance_metrics`

        If metric is "precomputed", X is assumed to be a distance matrix and
        must be square during fit.

        If metric is a callable function, it takes two arrays representing 1D
        vectors as inputs and must return one value indicating the distance
        between those vectors. This works for Scipy's metrics, but is less
        efficient than passing the metric name as a string.

        .. versionchanged:: 1.8
           Prior to 1.8, `metric=True/False` was used to select metric/non-metric
           MDS, which is now the role of `metric_mds`.  The support for ``True``
           and ``False`` will be dropped in version 1.10, use `metric_mds` instead.

    metric_params : dict, default=None
        Additional keyword arguments for the dissimilarity computation.

        .. versionadded:: 1.8

    normalized_stress : bool or "auto" default="auto"
        Whether to return normalized stress value (Stress-1) instead of raw