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

Code before:
    """Multidimensional scaling.

    Read more in the :ref:`User Guide <multidimensional_scaling>`.

    Parameters
    ----------
    n_components : int, default=2
        Number of dimensions in which to immerse the dissimilarities.

    metric_mds : bool, default=True
        If ``True``, perform metric MDS; otherwise, perform nonmetric MDS.
        When ``False`` (i.e. non-metric MDS), dissimilarities with 0 are considered as
        missing values.

        .. versionchanged:: 1.8
           The parameter `metric` was renamed into `metric_mds`.

    n_init : int, default=4
        Number of times the SMACOF algorithm will be run with different
        initializations. The final results will be the best output of the runs,
        determined by the run with the smallest final stress.

        .. versionchanged:: 1.9
           The default value for `n_init` will change from 4 to 1 in version 1.9.

    init : str, default='random'
        The initialization approach. If `random`, random initialization is used.
        If `classical_mds`, then classical MDS is run and used as initialization
        for MDS (in this case, the value of `n_init` is ignored).

        .. versionadded:: 1.8

        .. versionchanged:: 1.10
           The default value for `init` will change to `classical_mds`.

    max_iter : int, default=300
        Maximum number of iterations of the SMACOF algorithm for a single run.

    verbose : int, default=0
        Level of verbosity.

    eps : float, default=1e-6
        The tolerance with respect to stress (normalized by the sum of squared
        embedding distances) at which to declare convergence.

        .. versionchanged:: 1.7
           The default value for `eps` has changed from 1e-3 to 1e-6, as a result
           of a bugfix in the computation of the convergence criterion.

    n_jobs : int, default=None
        The number of jobs to use for the computation. If multiple

Review comment (suggestion):
```suggestion
    init : {'random', 'classical_mds'}, default='random'
```

Code after:
    """Multidimensional scaling.

    Read more in the :ref:`User Guide <multidimensional_scaling>`.

    Parameters
    ----------
    n_components : int, default=2
        Number of dimensions in which to immerse the dissimilarities.

    metric_mds : bool, default=True
        If ``True``, perform metric MDS; otherwise, perform nonmetric MDS.
        When ``False`` (i.e. non-metric MDS), dissimilarities with 0 are considered as
        missing values.

        .. versionchanged:: 1.8
           The parameter `metric` was renamed into `metric_mds`.

    n_init : int, default=4
        Number of times the SMACOF algorithm will be run with different
        initializations. The final results will be the best output of the runs,
        determined by the run with the smallest final stress.

        .. versionchanged:: 1.9
           The default value for `n_init` will change from 4 to 1 in version 1.9.

    init : {'random', 'classical_mds'}, default='random'
        The initialization approach. If `random`, random initialization is used.
        If `classical_mds`, then classical MDS is run and used as initialization
        for MDS (in this case, the value of `n_init` is ignored).

        .. versionadded:: 1.8

        .. versionchanged:: 1.10
           The default value for `init` will change to `classical_mds`.

    max_iter : int, default=300
        Maximum number of iterations of the SMACOF algorithm for a single run.

    verbose : int, default=0
        Level of verbosity.

    eps : float, default=1e-6
        The tolerance with respect to stress (normalized by the sum of squared
        embedding distances) at which to declare convergence.

        .. versionchanged:: 1.7
           The default value for `eps` has changed from 1e-3 to 1e-6, as a result
           of a bugfix in the computation of the convergence criterion.

    n_jobs : int, default=None
        The number of jobs to use for the computation. If multiple