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DOC Add efficiency improvements in release highlights 1.2 (scikit-learn#25013)
* add efficiency improvements in release highlights * Update plot_release_highlights_1_2_0.py
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‎examples/release_highlights/plot_release_highlights_1_2_0.py‎

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# The estimator can now run on any Array API compliant libraries such as
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# `CuPy <https://docs.cupy.dev/en/stable/overview.html>`__, a GPU-accelerated array
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# library. For details, see the :ref:`User Guide <array_api>`.
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# %%
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# Improved efficiency of many estimators
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# --------------------------------------
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# In version 1.1 the efficiency of many estimators relying on the computation of
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# pairwise distances was greatly improved for float64 dense input. In version 1.2,
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# the efficiency of these estimators was further improved for all combinations of
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# float32/float64 and dense/sparse input (for all metrics except euclidean). It
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# concerns essentially clustering, manifold learning and neighbor search algorithms.
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# A detailed list of the impacted estimators can be found in the
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# :ref:`changelog <changes_1_2>`. The main benefits are a reduced memory footprint
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# and a much better scalability on multi-core machines.

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