Change KMeans n_init default value to 1.

As mentioned in #9430, pydaal gets a speedup of 30x, mostly because they ignore n_init. I feel like n_init=10 is a pretty odd choice. We don’t really do random restarts anywhere else by default (afaik), and in the age of large datasets this is really pretty expensive.

Not sure if it’s worth a deprecation cycle but this is pretty non-obvious behavior that potentially makes us look bad.

[thomasjpfan]

In the example: Empirical evaluation of the impact of k-means initialization, it does show that n_init > 1 leads to an improvement for init="random". For k-means++, n_init does not make a difference. If we go by the example, then we can have a n_init="auto", where: n_init=1 for kmeans++ and n_init=10 for random.

[jnothman]

Yes, let’s change it. Or default to have an inverse relationship with dataset size.

On 12 September 2017 at 00:55, Andreas Mueller [e-mail] wrote:

As mentioned in #9430 https://github.com/scikit-learn/scikit-learn/issues/9430, pydaal gets a speedup of 30x, mostly because they ignore n_init. I feel like n_init=10 is a pretty odd choice. We don’t really do random restarts anywhere else by default (afaik), and in the age of large datasets this is really pretty expensive.

Not sure if it’s worth a deprecation cycle but this is pretty non-obvious behavior that potentially makes us look bad.

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