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doc/whats_new/v1.3.rst

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@@ -153,6 +153,27 @@ Changelog
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- |Enhancement| Added the parameter `fill_value` to :class:`impute.IterativeImputer`.
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:pr:`25232` by :user:`Thijs van Weezel <ValueInvestorThijs>`.
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:mod:`sklearn.linear_model`
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- |Enhancement| :class:`linear_model.LogisticRegression`,
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:class:`linear_model.LogisticRegressionCV`, :class:`linear_model.GammaRegressor`,
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:class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor` got
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a new solver `solver="newton-lsmr"`. This is a 2nd order (Newton) optimisation
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routine that uses the iterative LSMR algorithm: To find the Newton direction in each
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step, the 2nd order equation is cast as a least squares problem and solved
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iteratively, therefore called iteratively reweighted least squares (IRLS), via LSMR.
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Due to using LSMR, only matrix-vector multiplications are used and sparse matrices
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are supported as well. Especially for multiclass problems it might be worth a try.
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:pr:`25462` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |Enhancement| :class:`linear_model.GammaRegressor`,
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:class:`linear_model.PoissonRegressor` and :class:`linear_model.TweedieRegressor`
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as well as :class:`linear_model.LogisticRegression` and
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:class:`linear_model.LogisticRegressionCV` can reach higher precision with the lbfgs solver, in particular when `tol` is set
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to a tiny value. Moreover, `verbose` is now properly propagated to L-BFGS-B.
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:pr:`23619` by :user:`Christian Lorentzen <lorentzenchr>`.
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:mod:`sklearn.metrics`
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