@@ -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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+ ......................
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+
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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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+
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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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+
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:mod: `sklearn.metrics `
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