Highlight multiclass logistic regression with the newton-cholesky solver

ogrisel · ogrisel · commit f0669cecfa0b · 2024-12-05T16:27:20.000+01:00
diff --git a/examples/release_highlights/plot_release_highlights_1_6_0.py b/examples/release_highlights/plot_release_highlights_1_6_0.py
@@ -93,6 +93,27 @@
 # user specified validation sets. This feature is released early to collect feedback
 # from third-party libraries who might benefit from it.
 
+# %%
+# Multiclass support for `LogisticRegression(solver="newton-cholesky")`
+# ---------------------------------------------------------------------
+#
+# The `"newton-cholesky"` solver (originally introduced in scikit-learn version
+# 1.2) was previously limited to binary
+# :class:`~linear_model.LogisticRegression` and some other generalized linear
+# regression estimators (namely :class:`~linear_model.PoissonRegressor`,
+# :class:`~linear_model.GammaRegressor` and
+# :class:`~linear_model.TweedieRegressor`).
+#
+# This new release includes support for multiclass (multinomial)
+# :class:`~linear_model.LogisticRegression`.
+#
+# This solver is particularly useful when the number of features is small to
+# medium. It has been empirically shown to converge more reliably and faster
+# than other solvers on some medium sized datasets with one-hot encoded
+# categorical features as can be seen in the `benchmark results of the
+# pull-request
+# <https://github.com/scikit-learn/scikit-learn/pull/28840#issuecomment-2065368727>`_.
+
 # %%
 # Missing value support for Extra Trees
 # -------------------------------------
