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

Code before:
        .. 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.

        .. versionadded:: 1.8

    normalized_stress : bool or "auto" default="auto"
        Whether to return normalized stress value (Stress-1) instead of raw
        stress. By default, metric MDS returns raw stress while non-metric MDS
        returns normalized stress.

        .. versionadded:: 1.2

        .. versionchanged:: 1.4
           The default value changed from `False` to `"auto"` in version 1.4.

        .. versionchanged:: 1.7
           Normalized stress is now supported for metric MDS as well.

    Attributes
    ----------
    embedding_ : ndarray of shape (n_samples, n_components)
        Stores the position of the dataset in the embedding space.

    stress_ : float

Review comment (suggestion):
```suggestion
        .. 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.
```

Code after:
        .. 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
        stress. By default, metric MDS returns raw stress while non-metric MDS
        returns normalized stress.

        .. versionadded:: 1.2

        .. versionchanged:: 1.4
           The default value changed from `False` to `"auto"` in version 1.4.

        .. versionchanged:: 1.7
           Normalized stress is now supported for metric MDS as well.

    Attributes
    ----------
    embedding_ : ndarray of shape (n_samples, n_components)
        Stores the position of the dataset in the embedding space.

    stress_ : float
        The final value of the stress (sum of squared distance of the
        disparities and the distances for all constrained points).
        If `normalized_stress=True`, returns Stress-1.
        A value of 0 indicates "perfect" fit, 0.025 excellent, 0.05 good,