DOC Rephrase the param description of "name" in RocCurveDisplay

Related to https://github.com/[USER_REDACTED]n/scikit-learn/pull/30508 and this https://github.com/[USER_REDACTED]n/scikit-learn/pull/30508#discussion_r2772070275 in particular.

Better separate the 3 possibilities for the parameter and clearer description of how the para is used.

[USER_REDACTED] what do you think ?

@@ -60,15 +60,20 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
     name : str or list of str, default=None
         Name for labeling legend entries. The number of legend entries is determined
         by the `curve_kwargs` passed to `plot`, and is not affected by `name`.
-        To label each curve, provide a list of strings. To avoid labeling
-        individual curves that have the same appearance, a list cannot be used in
-        conjunction with `curve_kwargs` being a dictionary or None. If a
-        string is provided, it will be used to either label the single legend entry
-        or if there are multiple legend entries, label each individual curve with
-        the same name. If `None`, no name is shown in the legend.
+
+        If a string is provided, it will be used to either label the single legend
+        entry or if there are multiple legend entries, label each individual curve
+        with the same name.
+
+        If a list is provided, it will be used to label each curve individually.
+        Passing a list will raise an error if `curve_kwargs` is not a list to avoid
+        labeling individual curves that have the same appearance.
+
+        If `None`, no name is shown in the legend.

         .. versionchanged:: 1.7
-            `estimator_name` was deprecated in favor of `name`.
+            `estimator_name` was deprecated in favor of `name` and now accepts
+            a list for plotting multiple curves.

     pos_label : int, float, bool or str, default=None
         The class considered the positive class when ROC AUC metrics computed.
@@ -184,15 +189,19 @@ def plot(
             created.

         name : str or list of str, default=None
-            Name for labeling legend entries. The number of legend entries
-            is determined by `curve_kwargs`, and is not affected by `name`.
-            To label each curve, provide a list of strings. To avoid labeling
-            individual curves that have the same appearance, a list cannot be used in
-            conjunction with `curve_kwargs` being a dictionary or None. If a
-            string is provided, it will be used to either label the single legend entry
-            or if there are multiple legend entries, label each individual curve with
-            the same name. If `None`, set to `name` provided at `RocCurveDisplay`
-            initialization. If still `None`, no name is shown in the legend.
+            Name for labeling legend entries. The number of legend entries is determined
+            by the `curve_kwargs` passed to `plot`, and is not affected by `name`.
+
+            If a string is provided, it will be used to either label the single legend
+            entry or if there are multiple legend entries, label each individual curve
+            with the same name.
+
+            If a list is provided, it will be used to label each curve individually.
+            Passing a list will raise an error if `curve_kwargs` is not a list to avoid
+            labeling individual curves that have the same appearance.
+
+            If `None`, set to `name` provided at `RocCurveDisplay` initialization. If
+            still `None`, no name is shown in the legend.

             .. versionadded:: 1.7

@@ -678,14 +687,18 @@ def from_cv_results(
             created.

         name : str or list of str, default=None
-            Name for labeling legend entries. The number of legend entries
-            is determined by `curve_kwargs`, and is not affected by `name`.
-            To label each curve, provide a list of strings. To avoid labeling
-            individual curves that have the same appearance, a list cannot be used in
-            conjunction with `curve_kwargs` being a dictionary or None. If a
-            string is provided, it will be used to either label the single legend entry
-            or if there are multiple legend entries, label each individual curve with
-            the same name. If `None`, no name is shown in the legend.
+            Name for labeling legend entries. The number of legend entries is determined
+            by the `curve_kwargs` passed to `plot`, and is not affected by `name`.
+
+            If a string is provided, it will be used to either label the single legend
+            entry or if there are multiple legend entries, label each individual curve
+            with the same name.
+
+            If a list is provided, it will be used to label each curve individually.
+            Passing a list will raise an error if `curve_kwargs` is not a list to avoid
+            labeling individual curves that have the same appearance.
+
+            If `None`, no name is shown in the legend.

         curve_kwargs : dict or list of dict, default=None
             Keywords arguments to be passed to matplotlib's `plot` function

LGTM, I have also updated precision recall `name` parameters to use the same wording in v

LGTM, I accepted [USER_REDACTED]'s suggestion and enabled auto-merge.

Could you maybe rephrase the second part? Maybe something like: "In this case, `curve_kwargs` must also be a list in order to avoid labeling individual curves that share the same appearance."

Note: I am not really sure if my suggestion is correct.

What we want to stress is that it's not possible to pass a list if `curve_kwargs` is not a list, and your suggestion conveys something a bit different to me.

Maybe something more direct like
``"Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance"`` ?

Thanks, it is nicer split up.

What about

```
A list of strings labels each curve individually. Passing a list will raise an error if
`curve_kwargs` is not a list to avoid individually labeling curves that have the same appearance.
```

Can be done in separate PR but I think this needs to be added:
```suggestion
            `estimator_name` was deprecated in favor of `name` and now accepts
            a list for plotting multiple curves.
```


DOC Release highlights for 1.8

Topics I have thought of by [NAME_REDACTED] [changelog](https://scikit-learn.org/1.8/whats_new/v1.8.html), in no particular order

- [x] array API support, do we want to add a non-executed snippet with PyTorch (and maybe CalibratedClassifierCV with temperature scaling)?
- [x] free-threaded do something similar as 1.6 release highlights
- [x] temperature scaling with CalibratedClassifierCV https://github.com/[USER_REDACTED]n/scikit-learn/pull/31068
- [x] linear models improvements (suggested by [USER_REDACTED])
- [x] HTML repr improvements https://github.com/[USER_REDACTED]n/scikit-learn/pull/30763 and https://github.com/[USER_REDACTED]n/scikit-learn/pull/31564
- [x] tree regressor with MAE https://github.com/[USER_REDACTED]n/scikit-learn/pull/32100 and other bug-fixes
- [x] ClassicalMDS is shown as "[NAME_REDACTED]", so probably needs a release highlights entry https://github.com/[USER_REDACTED]n/scikit-learn/pull/31322

Other things I am not sure whether they are release highlights noteworthy, TSNE with PCA initialization, MDS with different metrics, QuadraticDiscriminantAnalysis improvements.

@@ -0,0 +1,288 @@
+# ruff: noqa: CPY001
+"""
+=======================================
+Release Highlights for scikit-learn 1.8
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.8! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <release_notes_1_8>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# Array API support (enables GPU computations)
+# --------------------------------------------
+# The progressive adoption of the Python array API standard in
+# scikit-learn means that PyTorch and CuPy input arrays
+# are used directly. This means that in scikit-learn estimators
+# and functions non-CPU devices, such as GPUs, can be used
+# to perform the computation. As a result performance is improved
+# and integration with these libraries is easier.
+#
+# In scikit-learn 1.8, several estimators and functions have been updated to
+# support array API compatible inputs, for example PyTorch tensors and CuPy
+# arrays.
+#
+# Array API support was added to the following estimators:
+# :class:`preprocessing.StandardScaler`,
+# :class:`preprocessing.PolynomialFeatures`, :class:`linear_model.RidgeCV`,
+# :class:`linear_model.RidgeClassifierCV`, :class:`mixture.GaussianMixture` and
+# :class:`calibration.CalibratedClassifierCV`.
+#
+# Array API support was also added to several metrics in :mod:`sklearn.metrics`
+# module, see :ref:`array_api_supported` for more details.
+#
+# Please refer to the :ref:`array API support<array_api>` page for instructions
+# to use scikit-learn with array API compatible libraries such as PyTorch or CuPy.
+# Note: Array API support is experimental and must be explicitly enabled both
+# in SciPy and scikit-learn.
+#
+# Here is an excerpt of using a feature engineering preprocessor on the CPU,
+# followed by :class:`calibration.CalibratedClassifierCV`
+# and :class:`linear_model.RidgeCV` together on a GPU with the help of PyTorch:
+#
+# .. code-block:: python
+#
+#     ridge_pipeline_gpu = make_pipeline(
+#         # Ensure that all features (including categorical features) are preprocessed
+#         # on the CPU and mapped to a numerical representation.
+#         feature_preprocessor,
+#         # Move the results to the GPU and perform computations there
+#         FunctionTransformer(
+#             lambda x: torch.tensor(x.to_numpy().astype(np.float32), device="cuda"))
+#         ,
+#         CalibratedClassifierCV(
+#             RidgeClassifierCV(alphas=alphas), method="temperature"
+#         ),
+#     )
+#     with sklearn.config_context(array_api_dispatch=True):
+#         cv_results = cross_validate(ridge_pipeline_gpu, features, target)
+#
+#
+# See the `full notebook on [NAME_REDACTED]
+# <https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing>`_
+# for more details. On this particular example, using the Colab GPU vs using a
+# single CPU core leads to a 10x speedup which is quite typical for such workloads.
+
+# %%
+# Free-threaded CPython 3.14 support
+# ----------------------------------
+#
+# scikit-learn has support for free-threaded CPython, in particular
+# free-threaded wheels are available for all of our supported platforms on Python
+# 3.14.
+#
+# We would be very interested by [NAME_REDACTED] a few things you can
+# try:
+#
+# - install free-threaded CPython 3.14, run your favourite
+#   scikit-learn script and check that nothing breaks unexpectedly.
+#   Note that CPython 3.14 (rather than 3.13) is strongly advised because a
+#   number of free-threaded bugs have been fixed since CPython 3.13.
+# - if you use some estimators with a `n_jobs` parameter, try changing the
+#   default backend to threading with `joblib.parallel_config` as in the
+#   snippet below. This could potentially speed-up your code because the
+#   default joblib backend is process-based and incurs more overhead than
+#   threads.
+#
+#   .. code-block:: python
+#
+#       grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)
+#       with joblib.parallel_config(backend="threading"):
+#           grid_search.fit(X, y)
+#
+# - don't hesitate to report any issue or unexpected performance behaviour by
+#   opening a `GitHub issue <https://github.com/[USER_REDACTED]n/scikit-learn/issues/new/choose>`_!
+#
+# Free-threaded (also known as nogil) CPython is a version of CPython that aims
+# to enable efficient multi-threaded use cases by [NAME_REDACTED]
+# [NAME_REDACTED] (GIL).
+#
+# For more details about free-threaded CPython see `py-free-threading doc
+# <https://py-free-threading.github.io>`_, in particular `how to install a
+# free-threaded CPython <https://py-free-threading.github.io/installing-cpython/>`_
+# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.
+#
+# In scikit-learn, one hope with free-threaded Python is to more efficiently
+# leverage multi-core CPUs by [NAME_REDACTED] of subprocess
+# workers for parallel computation when passing `n_jobs>1` in functions or
+# estimators. Efficiency gains are expected by [NAME_REDACTED]
+# inter-process communication. Be aware that switching the default joblib
+# backend and testing that everything works well with free-threaded Python is an
+# ongoing long-term effort.
+
+# %%
+# Temperature scaling in `CalibratedClassifierCV`
+# -----------------------------------------------
+# Probability calibration of classifiers with temperature scaling is available in
+# :class:`calibration.CalibratedClassifierCV` by setting `method="temperature"`.
+# This method is particularly well suited for multiclass problems because it provides
+# (better) calibrated probabilities with a single free parameter. This is in
+# contrast to all the other available calibrations methods
+# which use a "One-vs-Rest" scheme that adds more parameters for each class.
+
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.datasets import make_classification
+from sklearn.naive_bayes import GaussianNB
+
+X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
+clf = GaussianNB().fit(X, y)
+sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y)
+ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y)
+
+# %%
+# The following example shows that temperature scaling can produce better calibrated
+# probabilities than sigmoid calibration in multi-class classification problem
+# with 3 classes.
+
+import matplotlib.pyplot as plt
+
+from sklearn.calibration import CalibrationDisplay
+
+fig, axes = plt.subplots(
+    figsize=(8, 4.5),
+    ncols=3,
+    sharey=True,
+)
+for i, c in enumerate(ts.classes_):
+    CalibrationDisplay.from_predictions(
+        y == c, clf.predict_proba(X)[:, i], name="Uncalibrated", ax=axes[i], marker="s"
+    )
+    CalibrationDisplay.from_predictions(
+        y == c,
+        ts.predict_proba(X)[:, i],
+        name="Temperature scaling",
+        ax=axes[i],
+        marker="o",
+    )
+    CalibrationDisplay.from_predictions(
+        y == c, sig.predict_proba(X)[:, i], name="Sigmoid", ax=axes[i], marker="v"
+    )
+    axes[i].set_title(f"Class {c}")
+    axes[i].set_xlabel(None)
+    axes[i].set_ylabel(None)
+    axes[i].get_legend().remove()
+fig.suptitle("[NAME_REDACTED] per Class")
+fig.supxlabel("[NAME_REDACTED]")
+fig.supylabel("Fraction of Class")
+fig.legend(*axes[0].get_legend_handles_labels(), loc=(0.72, 0.5))
+plt.subplots_adjust(right=0.7)
+_ = fig.show()
+
+# %%
+# Efficiency improvements in linear models
+# ----------------------------------------
+# The fit time has been massively reduced for squared error based estimators
+# with L1 penalty: `ElasticNet`, `Lasso`, `MultiTaskElasticNet`,
+# `MultiTaskLasso` and their CV variants. The fit time improvement is mainly
+# achieved by **gap safe screening rules**. They enable the coordinate descent
+# solver to set feature coefficients to zero early on and not look at them
+# again. The stronger the L1 penalty the earlier features can be excluded from
+# further updates.
+
+from time import time
+
+from sklearn.datasets import make_regression
+from sklearn.linear_model import ElasticNetCV
+
+X, y = make_regression(n_features=10_000, random_state=0)
+model = ElasticNetCV()
+tic = time()
+model.fit(X, y)
+toc = time()
+print(f"[NAME_REDACTED] took {toc - tic:.3} seconds.")
+
+# %%
+# HTML representation of estimators
+# ---------------------------------
+# Hyperparameters in the dropdown table of the HTML representation now include
+# links to the online documentation. Docstring descriptions are also shown as
+# tooltips on hover.
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0, C=10))
+
+# %%
+# Expand the estimator diagram below by [NAME_REDACTED] "LogisticRegression" and then on
+# "Parameters".
+
+clf
+
+
+# %%
+# DecisionTreeRegressor with `criterion="absolute_error"`
+# ------------------------------------------------------
+# :class:`tree.DecisionTreeRegressor` with `criterion="absolute_error"`
+# now runs much faster. It has now `O(n * log(n))` complexity compared to
+# `O(n**2)` previously, which allows to scale to millions of data points.
+#
+# As an illustration, on a dataset with 100_000 samples and 1 feature, doing a
+# single split takes of the order of 100 ms, compared to ~20 seconds before.
+
+import time
+
+from sklearn.datasets import make_regression
+from sklearn.tree import DecisionTreeRegressor
+
+X, y = make_regression(n_samples=100_000, n_features=1)
+tree = DecisionTreeRegressor(criterion="absolute_error", max_depth=1)
+
+tic = time.time()
+tree.fit(X, y)
+elapsed = time.time() - tic
+print(f"Fit took {elapsed:.2f} seconds")
+
+# %%
+# ClassicalMDS
+# ------------
+# Classical MDS, also known as "[NAME_REDACTED]" (PCoA)
+# or "Torgerson's scaling" is now available within the `sklearn.manifold`
+# module. Classical MDS is close to PCA and instead of approximating
+# distances, it approximates pairwise scalar products, which has an exact
+# analytic solution in terms of eigendecomposition.
+#
+# Let's illustrate this new addition by [NAME_REDACTED] S-curve dataset to
+# get a low-dimensional representation of the data.
+
+import matplotlib.pyplot as plt
+from matplotlib import ticker
+
+from sklearn import datasets, manifold
+
+n_samples = 1500
+S_points, S_color = datasets.make_s_curve(n_samples, random_state=0)
+md_classical = manifold.ClassicalMDS(n_components=2)
+S_scaling = md_classical.fit_transform(S_points)
+
+fig = plt.figure(figsize=(8, 4))
+ax1 = fig.add_subplot(1, 2, 1, projection="3d")
+x, y, z = S_points.T
+ax1.scatter(x, y, z, c=S_color, s=50, alpha=0.8)
+ax1.set_title("Original S-curve samples", size=16)
+ax1.view_init(azim=-60, elev=9)
+for axis in (ax1.xaxis, ax1.yaxis, ax1.zaxis):
+    axis.set_major_locator(ticker.MultipleLocator(1))
+
+ax2 = fig.add_subplot(1, 2, 2)
+x2, y2 = S_scaling.T
+ax2.scatter(x2, y2, c=S_color, s=50, alpha=0.8)
+ax2.set_title("Classical MDS", size=16)
+for axis in (ax2.xaxis, ax2.yaxis):
+    axis.set_major_formatter(ticker.NullFormatter())
+
+plt.show()

First pass of contributions.

I don't have the permission to push into the PR branch directly, so I will leave it as a suggestion here.

Quick pass of typo/cosmetics fixes.

I don’t think we need the extra lines in the import sections for those snippents.

It seems nobody else added anything to the linear model section…

Some more nits. LGTM!

To be able to merge.

I approve of this message

"free-threaded wheel" is kind of a misnomer
How about
```suggestion
# scikit-learn has support for free-threaded CPython, in particular
# wheels with free-threaded builds are available for all of our supported platforms on Python
# 3.14.
```

Thanks for taking a look! Just curious, can you elaborate why this is a misnomer? It seems like it is used by [NAME_REDACTED] in the free-threaded world, for example [this page](https://hugovk.github.io/free-threaded-wheels/) by [NAME_REDACTED], a CPython developer. If we decide to change it we probably need to tweak the changelog because I mostly copied-and-pasted it from the changelog.

Also don't hesitate to push into this branch if you want to write the section about linear models improvements :wink:!

No strong opinion. For me, not the wheel itself is free-threded, but the wheel was build with (or against) free-threaded CPython.

I will sleep on your remark and see how I feel about it on Monday :wink:

Thanks for writing this! I am a bit undecided about mentioning the `Logisticregression{,CV}` deprecations in the release highlights.

Part of me thinks that deprecations are not so "crucial" and don't really belong in the release highlights. Part of me thinks that the `LogisticRegression{,CV}` changes may catch a few users and it would be nice to have a good place where they are summarized. Right now, my feeling is that they are a bit scattered in the changelog and in the docstrings.

Having slept on it, I think it's fine to keep "free-threaded wheels" since it seems used, even if it can be considered slightly imprecise.

I was even thinking about linking the issue where deprecation or not of `C` is discussed 😅

```suggestion
# to use scikit-learn with array API compatible libraries such as PyTorch or CuPy.
# Note that array API support is still experimental and must be
# explicitly be enabled both in SciPy and scikit-learn to work properly.
```

That's a good idea. We could prepare such a notebook against a nightly build of scikit-learn and update it after the release to point to the stable wheels instead.

I think we need to educate the reader about the what and why of array API.

```suggestion
# -----------------
# The progressive adoption of the Python array API standard in SciPy and
# scikit-learn allows the user to pass input arrays from conforming
# libraries to scikit-learn estimators and functions and let them
# use those libraries and possibly non-CPU devices such as GPUs to perform
# the computation instead of attempting to convert all inputs to NumPy.
#
# In scikit-learn 1.8, several estimators and functions have been updated to
# support array API compatible inputs, for example PyTorch tensors and CuPy
# arrays.
```

```suggestion
# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.
#
# The long term goal of free-threaded Python is to more efficiently leverage
# multi-core CPUs by [NAME_REDACTED] of subprocess workers for parallel
# computation when passing `n_jobs>1` in functions or estimators.
# Efficiency gains are expected by [NAME_REDACTED] inter-process communication.
# Note however that process-based parallelism is still the default joblib backend at
# the time of writing. You can call `joblib.parallel_config(backend="threading")` to
# change the default backend to "threading". Be aware that properly testing that
# everything is running smoothly when doing so is still an ongoing effort and that
# there are open issues to fix before considering making this the default.
```

We should link to the official website (https://data-apis.org/array-api/) in the first sentence but this can better be done from a real code editor if/when the above GitHub suggestion is accepted.

Maybe the order of the different paragraphs should be updated. But I think it's important to be a bit more explicit about the what and the why of this dev effort and manage expectations.

```suggestion
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# Probability calibration with temperature scaling is available in
# :class:`calibration.CalibratedClassifierCV` by setting `method = "temperature"`.
# This method offers a natural way to obtain (better-)calibrated multi-class
# probabilities with just one free parameter, in contrast to using a
# "One-vs-Rest" scheme that adds more parameters for each single class.

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.frozen import FrozenEstimator
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC

X, y = make_classification(
    n_samples=1000,
    n_features=10,
    n_informative=10,
    n_redundant=0,
    n_classes=5,
    n_clusters_per_class=1,
    class_sep=2.0,
    random_state=42,
)
X_train, X_calib, y_train, y_calib = train_test_split(X, y, random_state=42)
clf = LinearSVC(random_state=42)
clf.fit(X_train, y_train)
ts = CalibratedClassifierCV(FrozenEstimator(clf), method="temperature", ensemble=False)
ts.fit(X_calib, y_calib)
beta = ts.calibrated_classifiers_[0].calibrators[0].beta_
print(f"Optimal temperature = {1 / beta:.3}")
```

Thanks for the suggestions, which I accepted. For the record, something I was trying to do is to mention GPU early so that it clicks for readers. I'll try to have another look on how to do do this, maybe it can be done in the title of the section, like "Array API support (e.g. Pytorch GPU tensors or CuPy arrays)"?

```suggestion
# Probability calibration of classifiers with temperature scaling is available in
```

```suggestion
# This method is particularly well suited for multiclass problems because it it provides (better-) calibrated
```
I would avoid the word „natural“.

```suggestion
# "One-vs-Rest" scheme that adds more parameters for each class.
```

How about only specifying n_classes and use defaults for the rest?

I accepted [USER_REDACTED] suggestions, which make sense. IMO we should be in full collaborative editing mode, where everyone (with the necessary rights) should feel free to push directly into the branch :wink:.

[USER_REDACTED] I plan to work on such a notebook today.

Nice :pray:, something I was thinking of, but I am sure you have better ideas is a `Pipeline` using `PolynomialFeatures`, `StandardScaler` and `RidgeCV` since array API support for these estimators was all introduced in scikit-learn 1.8.

IMO, we don't need to have the most realistic pipeline (we can keep this one for a future blog post as we discussed) but something that shows that things work with PyTorch GPU tensors and it runs faster is already good enough.

If we link a google colab notebook (not the biggest fan of it), could we add code and output also here in the highlights to keep it self-contained?

I agree self-contained would be nice indeed but one of the constraint we have is that we don't have a GPU for the doc build so we can not execute the code as a normal example. [^1]

I think the different options (that can be mixed) as noted in the current code comment:
- add code with PyTorch cpu (with commented out code that can easily switch to PyTorch GPU even maybe mps on recent macOS M chips). You would need to add PyTorch to the doc lock-file, which is certainly doable but how important do we think this is? The release is approaching fast. Reminder: current plan is to release next Monday (December 8) and I think we are still on track for this timing.
- only use syntax-highlighted code that isn't executed but then you don't have the outputs or you need to add them manually doctest-style ...
- link to an external service that give you a free GPU, is there anything else and nicer than Colab out there (in case one of the underlying issue is to require a Google account)? The nice thing is the interactivity. How many people will actually click and try the example, it's hard to tell ...

[^1]: not sure it's worth it even longer term because the doc build is long ~45 minutes, we would need to pay for the GPU, and only a one (potentially a few examples in the future) use a GPU. Also the GPU we are using for CI right now is for GitHub Actions not CircleCI, no idea how easy this is to set-up something similar on CircleCI ...

I'd also vote for having the code in the release highlights, even if we can't execute it (is it possible to mark things as "don't execute this"?). Maybe not the whole notebook but the "highlight" part of it? I think we are too late to attempt adding pytorch as a doc dependency or doing other infrastructure work.

Colab and Kaggle are the only places I know where you can directly link to a notebook that is configured to use a GPU (there is a "trick" to having the link directly use a GPU instance so that users don't have to configure anything. I think starting the kernel on a GPU and then pressing save and then sharing? Something to double check before we hit publish).

Both require you to have an account and I think Colab is a nicer experience and more people have an account. But it is annoying that you force people to have a Google account. For a release highlight (in this page directly) I think having code where you can comment in/out code is too verbose. But we could having something like this (pytorch CPU by default, with commented CUDA and MPS versions) in the example gallery in the future (we should have thought of this earlier :-/)

I was thinking that a naive user will not know what the array API is, so using concrete library names that they recognise is better. The downside is that it might create the impression that those are the only supported ones :-/ However that is maybe not such a big problem given that we mostly focus on these (dask isn't there yet, jax needs more work).

The other dilemma is that I think keeping the text here short is good, which increases the chances of people reading it. But this also means that more people will read this and leave with half an impression because they don't read anything else. If we make this bit more extensive covering ifs and buts we get a more complete picture across but less people will read it :-/

This is my best effort at a compromise between shorter/all not 100% needed words removed and conveying a complete picture. WDYT?

```suggestion
# The progressive adoption of the Python array API standard in
# scikit-learn means that PyTorch and CuPy input arrays
# are used directly. This means that in scikit-learn estimators
# and functions non-CPU devices, such as GPUs, can be used
# to perform the computation. As a result performance is improved
# and integration with these libraries is easier.
```

```suggestion
# Note: Array API support is experimental and must be
# explicitly enabled both in SciPy and scikit-learn.
```
on a mission to remove words :)

The last sentence I added is trying to make it super obvious (in one short sentence) to the naive reader why this is a good thing. You could argue this should be at the start, to catch the eye of people who don't already know why array API support is an interesting topic.

A possible alternative way of arrange things. It satisfies my desire to have short sentences (it makes the German in me unhappy, but the Brit likes it) and maybe gets us closer to what Christian was looking for (on technical grounds I agree with him, but I can only come up with formulations that are a lot more clunky than "free-threaded wheels". However, the below seems reasonable

```suggestion
# scikit-learn has support for free-threaded CPython.
# [NAME_REDACTED] 3.14 wheels are available for all of our supported platforms.
```

```suggestion
# Free-threaded (also known as nogil) CPython is a version of CPython that aims to enable
```

I think you "aim at" when you point a physical thing at something (the most frequent use probably being a weapon that you aim at your enemy). When it is a goal I think you "aim to" - The other athletes aimed to beat [NAME_REDACTED] at the 400m hurdles by [NAME_REDACTED]

(I didn't include the next line, but we need to change "enabling" to "enable" if we make this change)

The simplest thing I can think of to have non-executed code is to put the code in a rst cell not a code cell (this is what I meant with syntax-highlighted code). Here is a precedent for the approach:
- [metadata routing in 1.6 highlights](https://scikit-learn.org/dev/auto_examples/release_highlights/plot_release_highlights_1_6_0.html#transforming-data-other-than-x-in-a-pipeline)
- [code](https://github.com/[USER_REDACTED]n/scikit-learn/blob/4a10d0ed8d85e6ed24a647bd28a65c0c64b101ef/examples/release_highlights/plot_release_highlights_1_6_0.py#L61-L86) is rst syntax to have syntax highlighting. I would have used `.. code-block:: python` for explicitness but it looks like it was done with indentation instead.

Thanks for the insights, for example I had no idea you could share a Colab that automatically choses the GPU runtime.

Also if we end up adding PyTorch to the doc build after the release, we can always back-port it in the release branch to improve the highlights.

```suggestion
# To try out scikit-learn's support for free-threading use Python 3.14.
#  Try your use cases and please `report your issues <https://github.com/[USER_REDACTED]n/scikit-learn/issues/new/choose>`_!
```
Less words mission. Should we link to the "new issue" page? Seems friendly to the user ~~but we don't have a lot of links in this page~~ (edit: I read the next paragraph 😆)

What is the goal of this paragraph? My impression is that it is to tell people that right now they need to reconfigure joblib and to make them aware that this is still in an early phase of development (in scikit-learn). If yes, should we remove the explanation of how free-threaded python offers the promise to remove process based workers and directly say something like: "You need to call `joblib.parallel_config(backend="threading")` to change the default backend to "threading" and use `n_jobs>1` to take advantage of free-threading for parallel computation. Note: free-threaded Python and support for it is brand new, this means that there are open issues to fix before making this the default."

```suggestion
# coordinate descent solver to set feature coefficients to 0 early and not look at them
```
(nitpick: spell out small numbers, so "zero" instead of "0" but don't ask me to justify this style rule, I can't)

```suggestion
# A further deprecation is related to the `penalty` parameter in both
```
Better, maybe?

Nice I like improving my English :heart:

```suggestion
X, y = make_classification(random_state=42)
```

A follow-up to [#32809 (comment)](https://github.com/[USER_REDACTED]n/scikit-learn/pull/32809/files#r2585022364): since `n_classes * n_clusters_per_class <=  2**n_informative`, the cleanest way to do this is probably to specify `random_state` only in here.

Olivier wrote this, and I guess the goal is to manage expectations. One tricky thing in this section is to hit the right balance between "this is cool, you should definitely try this!" because we would love user feed-back and "actually unless you turn a few knobs here and there, you will likely not notice any difference".

Basically, if you expect to switch to free-threading and your code to be faster, in most cases you will be disappointed, it may actually go a bit slower (e.g. JIT and free-threaded don't play nice with each other or something like this).

For completeness: here is a user report from July 2024 where "just switching to free-threaded" helped https://github.com/[USER_REDACTED]n/scikit-learn/issues/29587, but I guess it's a bit special.

Switching the default joblib backend may go faster by [NAME_REDACTED] processes. So if a user wants to do this and report issues, it would be more than welcome!

In the future joblib may use threading as the default backend (only for free-threaded I guess) but this is longer-term would need to test this a bit more. I did test it a bit the past but there were issues. Some of them were likely due to Python 3.13 and have gone away.

I tried to prototype semi-realistic pipelines but found bugs:

- #32836
- #32837

Assistance in investigating and quickly fixing them would help. In the meantime I will work on a minimal notebook to workaround them.

I think we can have both some non-executed code snippets + a link to a working colab notebook to run the code easily.

If we want people to try this (with the "this is experimental!" caveat) I think we should start the paragraph with what they have to do.

From what I understand:
- use Python 3.14
- `n_jobs>1`
- switch the joblib backend to threading

Is that right?

Maybe you don't have to switch the joblib backend and still get some improvements in some areas - but is it worth trying to explain this finer point to users vs telling them explicitly "do X, Y and Z". I think I am in the camp of "give clear instructions"

Thanks for your comments, they make a lot of sense. I'll try to have a closer look at improving the free-threaded section!

I think that most of what these comments explain were already included in version 1.7. The part that is new is the docstring as tooltip and link to the website documentation of each parameter.

I suggest to substitute lines 174-177 and 186-189 with something more like:

"_Hyperparameters in the dropdown table of the HTML representation now include links to the online documentation. Docstring descriptions are also shown as tooltips on hover.
To view an example, click on LogisticRegression and then on Parameters below._ "

Yeah it's probably the same confusion, I pushed a commit using your suggestion.

>> suggestions more than welcome

> Do you know if a GIF would work?

Ideally I was hoping keeping the current code that generates the diagram and "just find a way to add the js that expands the diagram" (no idea if that has a change of working), but honestly maybe this is good enough with a text description.

> use Python 3.14

This is not enough. You need to use a free-threaded build of CPython 3.14, not the usual one as explained in the linked doc.

I only found how to run js from a cell directly - that's it. More work needed as I don't know how to hide that code (it would be visible on the docs!) and other issues. But if the text is enough.. 😅

```suggestion

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB

X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
clf = GaussianNB()
sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y)  # old
ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y)  # new

# %%
# The following example shows that temperature scaling can produce better-calibrated
# probabilities than sigmoid calibration in multi-class classification problem
# (3 classes).

import matplotlib.pyplot as plt
from sklearn.calibration import CalibrationDisplay

fig, axes = plt.subplots(
    figsize=(8, 4.5),
    ncols=3,
    sharey=True,
)
for i, c in enumerate(ts.classes_):
    CalibrationDisplay.from_predictions(
        y == c,
        ts.predict_proba(X)[:, i],
        name="Temperature scaling",
        ax=axes[i],
    )
    CalibrationDisplay.from_predictions(
        y == c,
        sig.predict_proba(X)[:, i],
        name="Sigmoid",
        ax=axes[i],
    )
    axes[i].set_title(f"Class {c}")
    axes[i].set_xlabel(None)
    axes[i].set_ylabel(None)
    axes[i].get_legend().remove()
fig.suptitle("[NAME_REDACTED] per Class")
fig.supxlabel("[NAME_REDACTED]")
fig.supylabel("Fraction of Class")
fig.legend(*axes[0].get_legend_handles_labels(), loc=(0.72, 0.5))
plt.subplots_adjust(right=0.7)
_ = fig.show()
```

A preview of the visual:

<img width="800" height="450" alt="sphx_glr_plot_release_highlights_1_8_0_001" src="https://github.com/[USER_REDACTED]nts/assets/30bd1244-acc6-4f37-b816-7af1b82d8ffc" />

I think the temperature scaling highlight should use a multiclass setting where it really shines. For binary classification alone, I would not have introduced it.

Thanks [USER_REDACTED] and [USER_REDACTED].

My personal opinion is let's add an example without too much code for the release highlights. We should try our best to have release highlights eye-catching and not too long :wink:.

In a further PR, let's add an example that shows the multi-class setting where it really shines. Because you know what, I realized when working on the release that https://github.com/[USER_REDACTED]n/scikit-learn/pull/31068 was merged without any example actually using the feature. This must be one of the first time [^1] this happens in the whole history of scikit-learn :scream: :sweat_smile: :stuck_out_tongue_closed_eyes:

[^1]: probably not, but for new feature, I always thought a strong requirement is to have user guide, example and API doc. I am too lazy to double-checked but it's probably written somewhere even :wink:

How about the following:
Brief and concise application
```py
from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.svm import LinearSVC
X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
clf = LinearSVC(random_state=42)
sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y)  # old
ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y)  # new
```
And for visualization
```py
import matplotlib.pyplot as plt
from sklearn.calibration import CalibrationDisplay

fig, axes = plt.subplots(figsize=(8, 4.5), ncols=3, sharey=True)
for i, c in enumerate(ts.classes_):
    CalibrationDisplay.from_predictions(
        y==c,
        ts.predict_proba(X)[:, i],
        name=f"temperature scaling",
        ax=axes[i],
    )
    CalibrationDisplay.from_predictions(
        y==c,
        sig.predict_proba(X)[:, i],
        name=f"sigmoid",
        ax=axes[i],
    )
    axes[i].set_title(f"class {c}")
    axes[i].set_ylabel(f"Fraction of class {c}")
    axes[i].get_legend().remove()
    if i==0:
        axes[i].set_xlabel("Mean predicted probability")
    else:
        axes[i].set_xlabel(None)

fig.suptitle("[NAME_REDACTED] per class")
fig.legend(*axes[0].get_legend_handles_labels(), loc="outside lower right")
```
<img width="764" height="454" alt="image" src="https://github.com/[USER_REDACTED]nts/assets/0451e0ba-fb74-435d-9b06-82934edba002" />

Or shorter without the sigmoid.

Thank you all for the feedback!

I used [USER_REDACTED]'s suggestion to make the calibration improvement more obvious. I also split the code into two parts to cheat a bit on the "<= 50 lines" requirement :sweat_smile:.

I've updated my earlier suggestion directly to keep things tidy. The example is on the way.

I pushed 46c3425. Please let me know what you think.

```suggestion
# free-threaded CPython <https://py-free-threading.github.io/installing_cpython/>`_
```

```suggestion
# single split takes of the order of 100 ms, compared to ~20 seconds before.
```

```suggestion
# Here is a `full notebook of this example on [NAME_REDACTED]
# <https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing>`_.
# For this example, using the Colab T4 GPU vs using a single CPU core leads to a
# 8x speedup which is quite typical for such workloads.
```

I clicked through the notebook and liked it! Nice work! For me the link directly lead me to a H100 GPU which is nice. Sadly none were available so I got a L4 instead. Should we link directly to a L4 version to avoid that step? It seems like all the conclusions remain the same (it took about 67s with a CPU and 10s with GPU)

Below a slightly edited caveats section (a few typos and minimal wordsmithing). Wasn't sure where to put it.

---

Disclaimers:

- using `CalibratedClassifierCV` here is slight overkill: since the dataset is large a single shuffle split with a test set of 1000 points would be enough and yields the same D2 Brier score (but the effect of using the GPU would be not as impressive.)

- Ridge classifier could be turned into a valid probabilistic classifier by [NAME_REDACTED] prediction to the `[0, 1]` rang. We would not need the `CalibratedClassifierCV` wrapper at all while achieving an even better D2 Brier than our temperature calibrator. This is left for future work.

- the dataset is large, hence tuning the regularization parameter should not be necessary (fixing it to a small value should be enough) and would further allow the use of faster solvers for which we do not have array API support at the time of writing.

- finally, a logistic regression based pipeline with its default `tol` value and histogram gradient boosting on the raw features will learn competitive models with a favorable D2 Brier / fit time tradeoffs as well.

---

I switched to T4 which should be available for all with the free account. The speed-up is 8x in this case but I think this is fine.

I also incorporated your suggestions for the disclaimers.

What do you think of a comment like this that explains why this "obscure" `FunctionTransformer` is here and makes it clear (like super obviously mega clear) that the GPU support starts here?

```suggestion
#         # Move the results to the GPU and perform computations there
#         FunctionTransformer(
```

I'd also be happy without the comment but I think it could improve things.

Using a `FunctionTransformer` is a total nerd snipe to get someone to contribute a dedicated `MoveTheData` transformer :D :D :D

```suggestion
# A [full notebook of this example on Google
```

Just to avoid starting two paragraphs with "here".

```suggestion
# For this example, using the colab GPU vs using a single CPU core leads to a
```

I think the comment is a good idea.

TableVectorizer is from skrub. Shouldn‘t it be selfcontained?

```suggestion
# Here is an excerpt of using :class:`calibration.CalibratedClassifierCV` and
```

```suggestion
# We would be very interested by [NAME_REDACTED] a few things you can
```

```suggestion
#   Note that CPython 3.14 is strongly advised (vs 3.13) because a number of
```

```suggestion
#   opening a `GitHub issue <https://github.com/[USER_REDACTED]n/scikit-learn/issues/new/choose>`_!
```

```suggestion
# backend and testing that everything works well with free-threaded Python is a
```

```suggestion
# ongoing long-term effort.
```

```suggestion
# (better) calibrated probabilities with a single free parameter. This is in contrast to all the other available calibration `method`s which use a "One-vs-Rest" …
```
Needs a touch before committing.

```suggestion
# The following example shows that temperature scaling can produce better calibrated
```

```suggestion
# with 3 classes.
```

```suggestion
# coordinate descent solver to set feature coefficients to 0 early on and not look at them
```
Not 💯 sure if this is better.

```suggestion
```

```suggestion
# Classical MDS, also known as "[NAME_REDACTED]" (PCoA)
```

```suggestion
# module. Classical MDS is close to PCA and instead of approximating
```

```suggestion
# Let's illustrate this new addition by [NAME_REDACTED] S-curve dataset to
```

```suggestion
```

```suggestion
```

```suggestion
```

I think this looks good. I'll mark the thread as resolved so we can more easily see what is still "open"

For me it is ok to use something from skrub to make a "not too long" and a slightly more "what you'd find in the real world" example.

In the snippet, where the import statements are missing, we might need a comment or other "health warning" so that people realise that they need something extra? But then they also need pytorch installed to make this snippet work.

I'm undecided on how much detail to include :-/

The notebook is real cool. 🤩 There is a small typo:

The very first cell says "This notebook demonstrate"... shouldn't it be "demonstrate**s**"? (with an "s")

I'd keep it in the highlights. If 1.8 was the end of the story it might not be worth it, but more changes are happening and the way to handle them is a bit more unusual than a run of the mill deprecation (temporary argument).

That's a very good point I am adding it to the list of things to decide on.

I personally think it's OK, but we may make a link to the skrub TableVectorizer page and use explicitly `skrub.TableVectorizer` to make it more explicit that it's not a scikit-learn object. Would that make you happier [USER_REDACTED]?

I guess we could use a scikit-learn transformer, but then we need to update the Colab notebook, make sure it still shows some improvement update the text if the speed-up is not in the same ballpark etc ..., which is a little bit more work ...

I let you decide.
Background: I don't want to incur lot of extra work for basically nothing. I just noticed it and I think it is not necessary for what we want to show. In the past, the maintainers have been careful with promotion of other packages (no example/doc so far for statsmodels, skorch, sktime, darts, xgboost, lightgbm, ...). I could have place my package model-diagnostics here and there, but I didn't. Maybe I should (because really, you should look at those diagnostic plots!).

I think we should be consistent with the (non) promotion of other packages. In this case it is nice to use skrub, it saves lines of code but that argument is probably true for every case of using an outside package.

```suggestion
# coordinate descent solver to set feature coefficients to zero early on and not
```
For [USER_REDACTED] 😁

As a reminder (explicit is better than implicit). I am aiming to be able to release today :wink:. Not sure how realistic this is, but still this is my goal.

If it's documentation-related there are many things that we can fix after the release :wink:. In an ideal world we would be very happy about the release highlights before we announce the release on social media though.

Yeah I thought there was a comment like this and was not able to find it again somehow, let's do this.

I think this is the equivalent pipeline without skrub:

```python
from sklearn.pipeline import make_pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.preprocessing import QuantileTransformer, TargetEncoder, SplineTransformer
from sklearn.linear_model import RidgeClassifierCV
from sklearn.calibration import CalibratedClassifierCV

numeric_transformer = make_pipeline(
    QuantileTransformer(),
    SplineTransformer(n_knots=10),
)

categorical_transformer = TargetEncoder(cv=5)

preprocessor = ColumnTransformer(
    transformers=[
        ("numeric", numeric_transformer, make_column_selector(dtype_include="number")),
        ("categorical", categorical_transformer, make_column_selector(dtype_include="category")),
    ],
)

ridge_pipeline_cpu = make_pipeline(
    preprocessor,
    CalibratedClassifierCV(
        RidgeClassifierCV(alphas=alphas), method="temperature", cv=5
    ),
)
```

[USER_REDACTED] what do you think?

```suggestion
# See the `full notebook on [NAME_REDACTED]
```

```suggestion
# single CPU core leads to a 7x speedup which is quite typical for such
```

With skrub, the low cardinality categorical variable uses `OneHotEncoder` instead of `TargetEncoder`. I don't know how much that would impact the timings.

I liked the conciseness of the skrub based solution but maybe column transformer with explicit column names list it would be a bit less complex for such a small dataset.

I've updated the notebook to use the above code. I have to do something else for about 90min now, but then I'll update the snippet in the highlights (if someone else doesn't get there first).

```suggestion
#         # Ensure that all features (including categorical features) are preprocessed
#         # on the CPU and mapped to a numerical representation.
#         feature_preprocessor,
```

```suggestion
# Here is an excerpt of using a feature engineering preprocessor on the CPU,
# followed by :class:`calibration.CalibratedClassifierCV`
# and :class:`linear_model.RidgeCV` together on a GPU with the help of PyTorch:
```

Actually, with the updated feature engineering in the notebook, the speedup is back to 10x or more.

```suggestion
# single CPU core leads to a 10x speedup which is quite typical for such workloads.
```

"calibration `method` parameter values"
This is just tooooooooooo long.
```suggestion
# contrast to all the other available calibrations methods
```

Yeah I know it's just that I did not manage to get rst to do what I want `method`s (without a space before the "s") does not work. Did I mention I don't like rst, there you go, now it's out :sweat_smile:

The link to `how to install a free-threaded` links to a 404 response

```suggestion
# free-threaded CPython <https://py-free-threading.github.io/installing-cpython/>`_
```

Fixed. Thanks for finding this


CI Make linting bot comment only on failure

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#### What does this implement/fix? Explain your changes.

The linter bot no longer adds a comment when the linting succeeds, it only comments when the linting fails and removes its comment when the linting is fixed.

The code in `build_tools/get_comment.py` is also refactored to use the pyGithub package instead of http requests.

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Tested on my fork in https://github.com/[USER_REDACTED]ncoisPgm/scikit-learn/pull/5, although it might be tough to see it's working as the comments are now removed.

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@@ -58,7 +58,7 @@ jobs:
           python-version: 3.11

       - name: Install dependencies
-        run: python -m pip install requests
+        run: python -m pip install PyGithub

       - name: Create/update GitHub comment
         env:

@@ -1,11 +1,10 @@
 # This script is used to generate a comment for a PR when linting issues are
 # detected. It is used by the `Comment on failed linting` [NAME_REDACTED].
-# This script fails if there are not comments to be posted.

 import os
 import re

-import requests
+from github import Auth, Github, GithubException

 def get_versions(versions_file):
@@ -67,15 +66,15 @@ def get_step_message(log, start, end, title, message, details):
     return res

-def get_message(log_file, repo, pr_number, sha, run_id, details, versions):
+def get_message(log_file, repo_str, pr_number, sha, run_id, details, versions):
     with open(log_file, "r") as f:
         log = f.read()

     sub_text = (
         "\n\n<sub> _Generated for commit:"
-        f" [{sha[:7]}](https://github.com/{repo}/pull/{pr_number}/commits/{sha}). "
+        f" [{sha[:7]}](https://github.com/{repo_str}/pull/{pr_number}/commits/{sha}). "
         "Link to the linter CI: [here]"
-        f"(https://github.com/{repo}/actions/runs/{run_id})_ </sub>"
+        f"(https://github.com/{repo_str}/actions/runs/{run_id})_ </sub>"
     )

     if "### Linting completed ###" not in log:
@@ -189,12 +188,8 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions):
     )

     if not message:
-        # no issues detected, so this script "fails"
-        return (
-            "## ✔ [NAME_REDACTED]\n"
-            "All linting checks passed. Your pull request is in excellent shape! ☀"
-            + sub_text
-        )
+        # no issues detected, the linting succeeded
+        return None

     if not details:
         # This happens if posting the log fails, which happens if the log is too
@@ -216,104 +211,58 @@ def get_message(log_file, repo, pr_number, sha, run_id, details, versions):
         + "https://scikit-learn.org/dev/developers/development_setup.html#set-up-pre-commit)"
         + ".\n\n"
         + "You can see the details of the linting issues under the `lint` job [here]"
-        + f"(https://github.com/{repo}/actions/runs/{run_id})\n\n"
+        + f"(https://github.com/{repo_str}/actions/runs/{run_id})\n\n"
         + message
         + sub_text
     )

     return message

-def get_headers(token):
-    """Get the headers for the GitHub API."""
-    return {
-        "Accept": "application/vnd.github+json",
-        "Authorization": f"Bearer {token}",
-        "X-GitHub-Api-Version": "2022-11-28",
-    }
-
-
-def find_lint_bot_comments(repo, token, pr_number):
+def find_lint_bot_comments(issue):
     """Get the comment from the linting bot."""
-    # repo is in the form of "org/repo"
-    # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#list-issue-comments
-    response = requests.get(
-        f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments",
-        headers=get_headers(token),
-    )
-    response.raise_for_status()
-    all_comments = response.json()

     failed_comment = "❌ Linting issues"
-    success_comment = "✔ [NAME_REDACTED]"
-
-    # Find all comments that match the linting bot, and return the first one.
-    # There should always be only one such comment, or none, if the PR is
-    # just created.
-    comments = [
-        comment
-        for comment in all_comments
-        if comment["user"]["login"] == "github-actions[bot]"
-        and (failed_comment in comment["body"] or success_comment in comment["body"])
-    ]
-
-    if len(all_comments) > 25 and not comments:
-        # By [NAME_REDACTED] the first 30 comments. If we can't find the
-        # comment created by [NAME_REDACTED], then we raise and we skip creating
-        # a comment in the first place.
-        raise RuntimeError("Comment not found in the first 30 comments.")
-
-    return comments[0] if comments else None
-
-
-def create_or_update_comment(comment, message, repo, pr_number, token):
-    """Create a new comment or update existing one."""
-    # repo is in the form of "org/repo"
+
+    for comment in issue.get_comments():
+        if comment.user.login == "github-actions[bot]":
+            if failed_comment in comment.body:
+                return comment
+
+    return None
+
+
+def create_or_update_comment(comment, message, issue):
+    """Create a new comment or update the existing linting comment."""
+
     if comment is not None:
-        print("updating existing comment")
-        # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#update-an-issue-comment
-        response = requests.patch(
-            f"https://api.github.com/repos/{repo}/issues/comments/{comment['id']}",
-            headers=get_headers(token),
-            json={"body": message},
-        )
+        print("Updating existing comment")
+        comment.edit(message)
     else:
-        print("creating new comment")
-        # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#create-an-issue-comment
-        response = requests.post(
-            f"https://api.github.com/repos/{repo}/issues/{pr_number}/comments",
-            headers=get_headers(token),
-            json={"body": message},
-        )
+        print("Creating new comment")
+        issue.create_comment(message)

-    response.raise_for_status()

+def update_linter_fails_label(linting_failed, issue):
+    """Add or remove the label indicating that the linting has failed."""

-def update_linter_fails_label(message, repo, pr_number, token):
-    """ "Add or remove the label indicating that the linting has failed."""
+    label = "CI:Linter failure"
+
+    if linting_failed:
+        issue.add_to_labels(label)

-    if "❌ Linting issues" in message:
-        # API doc: https://docs.github.com/en/rest/issues/labels?apiVersion=2022-11-28#add-labels-to-an-issue
-        response = requests.post(
-            f"https://api.github.com/repos/{repo}/issues/{pr_number}/labels",
-            headers=get_headers(token),
-            json={"labels": ["CI:Linter failure"]},
-        )
-        response.raise_for_status()
     else:
-        # API doc: https://docs.github.com/en/rest/issues/labels?apiVersion=2022-11-28#remove-a-label-from-an-issue
-        response = requests.delete(
-            f"https://api.github.com/repos/{repo}/issues/{pr_number}/labels/CI:Linter"
-            " failure",
-            headers=get_headers(token),
-        )
-        # If the label was not set, trying to remove it returns a 404 error
-        if response.status_code != 404:
-            response.raise_for_status()
+        try:
+            issue.remove_from_labels(label)
+        except GithubException as exception:
+            # The exception is ignored if raised because the issue did not have the
+            # label already
+            if not exception.message == "Label does not exist":
+                raise

 if __name__ == "__main__":
-    repo = os.environ["GITHUB_REPOSITORY"]
+    repo_str = os.environ["GITHUB_REPOSITORY"]
     token = os.environ["GITHUB_TOKEN"]
     pr_number = os.environ["PR_NUMBER"]
     sha = os.environ["BRANCH_SHA"]
@@ -323,58 +272,60 @@ def update_linter_fails_label(message, repo, pr_number, token):

     versions = get_versions(versions_file)

-    if not repo or not token or not pr_number or not log_file or not run_id:
-        raise ValueError(
-            "One of the following environment variables is not set: "
-            "GITHUB_REPOSITORY, GITHUB_TOKEN, PR_NUMBER, LOG_FILE, RUN_ID"
-        )
+    for var, val in [
+        ("GITHUB_REPOSITORY", repo_str),
+        ("GITHUB_TOKEN", token),
+        ("PR_NUMBER", pr_number),
+        ("LOG_FILE", log_file),
+        ("RUN_ID", run_id),
+    ]:
+        if not val:
+            raise ValueError(f"The following environment variable is not set: {var}")

     if not re.match(r"\d+$", pr_number):
         raise ValueError(f"PR_NUMBER should be a number, got {pr_number!r} instead")
+    pr_number = int(pr_number)
+
+    gh = Github(auth=Auth.Token(token))
+    repo = gh.get_repo(repo_str)
+    issue = repo.get_issue(number=pr_number)
+
+    message = get_message(
+        log_file,
+        repo_str=repo_str,
+        pr_number=pr_number,
+        sha=sha,
+        run_id=run_id,
+        details=True,
+        versions=versions,
+    )

-    try:
-        comment = find_lint_bot_comments(repo, token, pr_number)
-    except RuntimeError:
-        print("Comment not found in the first 30 comments. Skipping!")
-        exit(0)
-
-    try:
-        message = get_message(
-            log_file,
-            repo=repo,
-            pr_number=pr_number,
-            sha=sha,
-            run_id=run_id,
-            details=True,
-            versions=versions,
-        )
-        create_or_update_comment(
-            comment=comment,
-            message=message,
-            repo=repo,
-            pr_number=pr_number,
-            token=token,
-        )
-        print(message)
-    except requests.HTTPError:
-        # The above fails if the message is too long. In that case, we
-        # try again without the details.
-        message = get_message(
-            log_file,
-            repo=repo,
-            pr_number=pr_number,
-            sha=sha,
-            run_id=run_id,
-            details=False,
-            versions=versions,
-        )
-        create_or_update_comment(
-            comment=comment,
-            message=message,
-            repo=repo,
-            pr_number=pr_number,
-            token=token,
-        )
-        print(message)
+    update_linter_fails_label(
+        linting_failed=message is not None,
+        issue=issue,
+    )
+
+    comment = find_lint_bot_comments(issue)

-    update_linter_fails_label(message, repo, pr_number, token)
+    if message is None:  # linting succeeded
+        if comment is not None:
+            print("Deleting existing comment.")
+            comment.delete()
+    else:
+        try:
+            create_or_update_comment(comment, message, issue)
+            print(message)
+        except GithubException:
+            # The above fails if the message is too long. In that case, we
+            # try again without the details.
+            message = get_message(
+                log_file,
+                repo=repo,
+                pr_number=pr_number,
+                sha=sha,
+                run_id=run_id,
+                details=False,
+                versions=versions,
+            )
+            create_or_update_comment(comment, message, issue)
+            print(message)

Looks nice, thanks!

Nitpick: using a bare `raise` creates a traceback which is a tiny bit simpler (basically you don't get the `raise exception` line)

```suggestion
                raise
```


ENH: Order user-set parameters before default parameters on HTML Display

#### [NAME_REDACTED]/PRs

This was suggested during a face to face conversation. Instead of using the `__init__` signature order, place the non-default parameters first. Note - the non-default parameters are the ones with orange font color.
For example (see `C` and `max_iter`):

Order them like this:

#### What does this implement/fix? Explain your changes.

#### Any other comments?
[USER_REDACTED] and [USER_REDACTED] told me about this. :)

@@ -318,19 +318,30 @@ def is_non_default(param_name, param_value):

             return False

-        # reorder the parameters from `self.get_params` using the `__init__`
-        # signature
-        remaining_params = [name for name in out if name not in init_default_params]
-        ordered_out = {name: out[name] for name in init_default_params if name in out}
-        ordered_out.update({name: out[name] for name in remaining_params})
-
-        non_default_ls = tuple(
-            [name for name, value in ordered_out.items() if is_non_default(name, value)]
+        # Sort parameters so non-default parameters are shown first
+        unordered_params = {
+            name: out[name] for name in init_default_params if name in out
+        }
+        unordered_params.update(
+            {
+                name: value
+                for name, value in out.items()
+                if name not in init_default_params
+            }
         )

+        non_default_params, default_params = [], []
+        for name, value in unordered_params.items():
+            if is_non_default(name, value):
+                non_default_params.append(name)
+            else:
+                default_params.append(name)
+
+        params = {name: out[name] for name in non_default_params + default_params}
+
         return ParamsDict(
-            params=ordered_out,
-            non_default=non_default_ls,
+            params=params,
+            non_default=tuple(non_default_params),
             estimator_class=self.__class__,
             doc_link=doc_link,
         )

Thanks [USER_REDACTED], this will certainly improve the user experience!

Here is the suggestion from Claude to improve code readability. I checked this solution and it also seems to work, but I haven't run the tests. I let you iterate on this suggestion or dismiss it if you don't find it suited.

Thanks again [USER_REDACTED]! I'll wait for [USER_REDACTED] to also have a look on it.

```suggestion
```

```suggestion
        ordered_params = [name for name in init_default_params if name in out]
        ordered_params.extend(name for name in out if name not in init_default_params)
```

```suggestion
            name for name in ordered_params if is_non_default(name, out[name])
        )

        non_default_set = set(non_default_ls)  # For O(1) lookup
        ordered_params.sort(key=lambda name: name not in non_default_set)
        output_dict = {name: out[name] for name in ordered_params}
```

Thank you [USER_REDACTED]..
I think that we need to keep `remaining_params` in case there are parameters defined outside of `__init__`.

I reviewed the suggested code. It is interesting but it does the same thing that already works. So I would like to keep what I have.

Actually, I took this suggestion - thanks.

I would do 2 improvements:

- in terms of naming to avoid naming object by [NAME_REDACTED]
- avoid multiple for loop to iterate on the same objects

```python
        non_default_params, default_params = [], []
        for name, value in ordered_dict.items():
            if is_non_default(name, value):
                non_default_params.append(name)
            else:
                default_params.append(name)

        params = {name: out[name] for name in non_default_params + default_params}
```

So we can make a single loop to get both default and non-default params and concatenate this list to create the final dictionary in one go.

```suggestion
            {name: value for name, value in out.items() if name not in init_default_params}
```

I think that we need to update this comment stating that we are going to put the non-default first.

Let's call this dict `unordered_params` since it is the not yet the ordering that we which.

Done.
Thank you for the feedback

Ops! that one was obvious...
Done.

Done.

Done.


MNT clean up double calls to `check_array` in `brier_score_loss`, `d2_brier_score` and `d2_log_loss_score`

#### [NAME_REDACTED]/PRs
https://github.com/[USER_REDACTED]n/scikit-learn/pull/32549#issuecomment-3428908083

#### What does this implement/fix? Explain your changes.
This PR cleans up double calls to `check_array(y_pred, ...)` in `brier_score_loss`, `d2_brier_score`  and `d2_log_loss_score`, that had been introduced probably by [NAME_REDACTED]

In addition, I have seen that the call to `check_array(transformed_labels)` is probably unnecessary. I have checked `_one_hot_encoding_multiclass_target()` and there is no possibility of the return value not being a proper array, I think.

#### Details ####
Since, `y_pred` needs to be checked early in these scoring functions, I have decided to remove the second call to `check_array` from `_validate_multiclass_probabilistic_prediction` which is called later from of the functions. As an additional argument, `_validate_binary_probabilistic_prediction` doesn't check arrays either and the solution I have chosen is cleaner.

This required to add the check to `log_loss`, which previously had only relied on `_validate_multiclass_probabilistic_prediction` for checking `y_pred`.

@@ -251,7 +251,7 @@ def _validate_multiclass_probabilistic_prediction(
     y_true : array-like or label indicator matrix
         Ground truth (correct) labels for n_samples samples.

-    y_prob : array-like of float, shape=(n_samples, n_classes) or (n_samples,)
+    y_prob : array of floats, shape=(n_samples, n_classes) or (n_samples,)
         Predicted probabilities, as returned by a classifier's
         predict_proba method. If `y_prob.shape = (n_samples,)`
         the probabilities provided are assumed to be that of the
@@ -275,10 +275,6 @@ def _validate_multiclass_probabilistic_prediction(
     """
     xp, _, device_ = get_namespace_and_device(y_prob)

-    y_prob = check_array(
-        y_prob, ensure_2d=False, dtype=supported_float_dtypes(xp, device=device_)
-    )
-
     if xp.max(y_prob) > 1:
         raise ValueError(f"y_prob contains values greater than 1: {xp.max(y_prob)}")
     if xp.min(y_prob) < 0:
@@ -317,7 +313,6 @@ def _validate_multiclass_probabilistic_prediction(
         )

     # Check if dimensions are consistent.
-    transformed_labels = check_array(transformed_labels)
     if lb_classes.shape[0] != y_prob.shape[1]:
         if labels is None:
             raise ValueError(
@@ -3373,8 +3368,11 @@ def log_loss(y_true, y_pred, *, normalize=True, sample_weight=None, labels=None)
     ...          [[.1, .9], [.9, .1], [.8, .2], [.35, .65]])
     0.21616
     """
+    xp, _, device_ = get_namespace_and_device(y_pred)
+    y_pred = check_array(
+        y_pred, ensure_2d=False, dtype=supported_float_dtypes(xp, device=device_)
+    )
     if sample_weight is not None:
-        xp, _, device_ = get_namespace_and_device(y_pred)
         sample_weight = move_to(sample_weight, xp=xp, device=device_)

     transformed_labels, y_pred = _validate_multiclass_probabilistic_prediction(
@@ -3856,9 +3854,11 @@ def d2_log_loss_score(y_true, y_pred, *, sample_weight=None, labels=None):
         warnings.warn(msg, UndefinedMetricWarning)
         return float("nan")

-    y_pred = check_array(y_pred, ensure_2d=False, dtype="numeric")
+    xp, _, device_ = get_namespace_and_device(y_pred)
+    y_pred = check_array(
+        y_pred, ensure_2d=False, dtype=supported_float_dtypes(xp, device=device_)
+    )
     if sample_weight is not None:
-        xp, _, device_ = get_namespace_and_device(y_pred)
         sample_weight = move_to(sample_weight, xp=xp, device=device_)

     transformed_labels, y_pred = _validate_multiclass_probabilistic_prediction(

I'm okay with removing `check_array` out of `_validate_multiclass_probabilistic_prediction` and let the caller do it.

Thanks, [USER_REDACTED]!

Just a small nitpick. Otherwise, LGTM!

LGTM. Thank you [USER_REDACTED]

Can we update the docstring for `_validate_multiclass_probabilistic_prediction` stating that `y_prob` must be an array and not just an "array-like"?

Oh yes, I have just done that.

```suggestion
    y_prob : array of floats, shape=(n_samples, n_classes) or (n_samples,)
```

Thanks, [USER_REDACTED] !


DOC Update Contributing intro and Ways to contribute

#### [NAME_REDACTED]/PRs
Follow up to #32715, specifically see https://github.com/[USER_REDACTED]n/scikit-learn/pull/32715#discussion_r2538148882

#### What does this implement/fix? Explain your changes.

* Removes the line "and everyone is welcome to contribute"
* Make it more clear that this is not a good place to start for people new contributors

#### Any other comments?

I understand that this change may be a bit controversial and I am happy to discuss and iterate, this is a first attempt only. I've tried to keep the changes minimal.

(NB I think the changes suggested in https://github.com/[USER_REDACTED]n/scikit-learn/pull/32715#discussion_r2538249454 to the "Our community, our values" section can be made in a follow up PR)

cc [USER_REDACTED] [USER_REDACTED] [USER_REDACTED] [USER_REDACTED] who reviewed #32715
And [USER_REDACTED] [USER_REDACTED] who may be interested

@@ -24,15 +24,16 @@ Contributing

 .. currentmodule:: sklearn

-This project is a community effort, and everyone is welcome to
-contribute. It is hosted on https://github.com/[USER_REDACTED]n/scikit-learn.
+This project is a community effort, shaped by a large number of contributors from
+across the world. For more information on the history and people behind scikit-learn
+see :ref:`about`. It is hosted on https://github.com/[USER_REDACTED]n/scikit-learn.
 The decision making process and governance structure of scikit-learn is laid
 out in :ref:`governance`.

 Scikit-learn is :ref:`selective <selectiveness>` when it comes to
 adding new algorithms and features. This means the best way to contribute
 and help the project is to start working on known issues.
-See :ref:`new_contributors` to get started.
+See :ref:`ways_to_contribute` to learn how to make meaningful contributions.

 .. topic:: **Our community, our values**

@@ -54,49 +55,33 @@ See :ref:`new_contributors` to get started.
     Communications on all channels should respect our `Code of Conduct
     <https://github.com/[USER_REDACTED]n/scikit-learn/blob/main/CODE_OF_CONDUCT.md>`_.

-
-
-In case you experience issues using this package, do not hesitate to submit a
-ticket to the
-`GitHub issue tracker
-<https://github.com/[USER_REDACTED]n/scikit-learn/issues>`_. You are also
-welcome to post feature requests or pull requests.
-
 .. _ways_to_contribute:

 Ways to contribute
 ==================

-There are many ways to contribute to scikit-learn. Improving the
-documentation is no less important than improving the code of the library
-itself. If you find a typo in the documentation, or have made improvements, do
-not hesitate to create a GitHub issue or preferably submit a GitHub pull request.
-
-There are many ways to help. In particular helping to
-:ref:`improve, triage, and investigate issues <bug_triaging>` and
-:ref:`reviewing other developers' pull requests <code_review>` are very
-valuable contributions that move the project forward.
-
-Another way to contribute is to report issues you are facing, and give a "thumbs
-up" on issues that others reported and that are relevant to you.  It also helps
-us if you spread the word: reference the project from your blog and articles,
-link to it from your website, or simply star to say "I use it":
-
-.. raw:: html
-
-  <p>
-    <object
-      data="https://img.shields.io/github/stars/scikit-learn/scikit-learn?style=for-the-badge&logo=github"
-      type="image/svg+xml">
-    </object>
-  </p>
-
-In case a contribution/issue involves changes to the API principles
-or changes to dependencies or supported versions, it must be backed by a
-:ref:`slep`, where a SLEP must be submitted as a pull-request to
-`enhancement proposals <https://scikit-learn-enhancement-proposals.readthedocs.io>`_
-using the `SLEP template <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep_template.html>`_
-and follows the decision-making process outlined in :ref:`governance`.
+There are many ways to contribute to scikit-learn. These include:
+
+* referencing scikit-learn from your blog and articles, linking to it from your website,
+  or simply
+  `staring it <https://docs.github.com/en/get-started/exploring-projects-on-github/saving-repositories-with-stars>`__
+  to say "I use it"; this helps us promote the project
+* :ref:`improving and investigating issues <bug_triaging>`
+* :ref:`reviewing other developers' pull requests <code_review>`
+* reporting difficulties when using this package by [NAME_REDACTED]
+  `issue <https://github.com/[USER_REDACTED]n/scikit-learn/issues>`__, and giving a
+  "thumbs up" on issues that others reported and that are relevant to you (see
+  :ref:`submitting_bug_feature` for details)
+* improving the :ref:`contribute_documentation`
+* making a code contribution
+
+There are many ways to contribute without writing code, and we value these
+contributions just as highly as code contributions. If you are interested in making
+a code contribution, please keep in mind that scikit-learn has evolved into a mature
+and complex project since its inception in 2007. Contributing to the project code
+generally requires advanced skills, and it may not be the best place to begin if you
+are new to open source contribution. In this case we suggest you follow the suggestions
+in :ref:`new_contributors`.

 .. dropdown:: Contributing to related projects

@@ -125,16 +110,32 @@ [NAME_REDACTED]
 ----------------

 We recommend new contributors start by [NAME_REDACTED], in
-particular :ref:`ways_to_contribute`, :ref:`automated_contributions_policy`
-and :ref:`pr_checklist`. For expected etiquette around which issues and stalled PRs
+particular :ref:`ways_to_contribute`, :ref:`automated_contributions_policy`.
+
+Next, we advise new contributors gain foundational knowledge on
+scikit-learn and open source by:
+
+* :ref:`improving and investigating issues <bug_triaging>`
+
+  * confirming that a problem reported can be reproduced and providing a
+    :ref:`minimal reproducible code <minimal_reproducer>` (if missing), can help you
+    learn about different use cases and user needs
+  * investigating the root cause of an issue will aid you in familiarising yourself
+    with the scikit-learn codebase
+
+* :ref:`reviewing other developers' pull requests <code_review>` will help you
+  develop an understanding of the requirements and quality expected of contributions
+* improving the :ref:`contribute_documentation` can help deepen your knowledge
+  of the statistical concepts behind models and functions, and scikit-learn API
+
+If you wish to make code contributions after building your foundational knowledge, we
+recommend you start by [NAME_REDACTED] that is of interest to you, in an area you
+are already familiar with as a user or have background knowledge of. We recommend
+starting with smaller pull requests and following our :ref:`pr_checklist`.
+For expected etiquette around which issues and stalled PRs
 to work on, please read :ref:`stalled_pull_request`, :ref:`stalled_unclaimed_issues`
 and :ref:`issues_tagged_needs_triage`.

-We understand that everyone has different interests and backgrounds, thus we recommend
-you start by [NAME_REDACTED] that is of interest to you, in an area you are
-already familiar with as a user or have background knowledge of. We recommend starting
-with smaller pull requests, to get used to the contribution process.
-
 We rarely use the "good first issue" label because it is difficult to make
 assumptions about new contributors and these issues often prove more complex
 than originally anticipated. It is still useful to check if there are
@@ -169,6 +170,8 @@ source project.
 Please self review all code or documentation changes made by [NAME_REDACTED] them under your name.

+.. _submitting_bug_feature:
+
 Submitting a bug report or a feature request
 ============================================

@@ -195,6 +198,13 @@ following rules before submitting:
 -  If you are submitting a bug report, we strongly encourage you to follow the guidelines in
    :ref:`filing_bugs`.

+When a feature request involves changes to the API principles
+or changes to dependencies or supported versions, it must be backed by a
+:ref:`SLEP <slep>`, which must be submitted as a pull-request to
+`enhancement proposals <https://scikit-learn-enhancement-proposals.readthedocs.io>`_
+using the `SLEP template <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep_template.html>`_
+and follows the decision-making process outlined in :ref:`governance`.
+
 .. _filing_bugs:

 How to make a good bug report

Thanks [USER_REDACTED]!

I think this is sadly a necessary change, though it stings a quite bit to fully remove being open for less experienced people, since it really depends on the attitude more than the skills.

Assuming it is the more careful and thoughtful new contributors who even read this part of the developer docs, I find it important to address them. I have made a suggestion in the comments on still be welcoming but setting expectations on the same time. Curious to know what you think.

Looks nice, all these improvements are super useful, thanks a lot!

I still have some hope trying to devote time to reorganizing the contributing doc (maybe one day?), but in the mean time these improvements are great!

Thank you, [USER_REDACTED]! I have two more small wording suggestions, otherwise I think it looks good, and it adds a lot to setting expectations for new contributors.

It reads great and the rendered docs look good. Thanks everyone. :)
Shall we merge this?

Not sure this sentence is needed:

> For more information on the history and people behind scikit-learn
see :ref:`about`.

I mostly wanted to add a link to 'about'. Happy to remove or amend.

I love a bullet list - I find it easier to read, but open to changes here.

I thought about including 'making feature request' in the bullet but I am not sure we want to encourage this. Note that the reference 'submitting_bug_feature' is to the section "Submitting a bug report or a feature request" which does talk about making a feature request.

This did not feel right here, with the introduction because it's quite technical. I could not find a good place to put it, with the current contributing guide. I've moved it to the section "Submitting a bug report or a feature request", which I think is okay for now.

It may be suitable in a (new) section about 'feature requests' (which we don't really have, though we do mention in 2 or 3 places about being selective about what we include in the project and why). It may also be useful to link to if we add a contributing code section (e.g., in the intro).

I've amended the wording here, ideally something big enough to require a SLEP should not get to the 'contributing' stage (without a SLEP).

```suggestion
and it may not be the best place to start if you are a beginner. If you are
less experienced you are still very welcome, just be prepared to invest
significant effort and research independently.
```

What about this, will this strike a balance between setting expectations and still being open to everybody?

That doesn't render correctly, I think the _ will fix that.
```suggestion
  `issue <https://github.com/[USER_REDACTED]n/scikit-learn/issues>`_, and giving a
```

I am happy to add this.

I forgot to mention but I intentionally put code contributions at the bottom of the list because I think the other ways to contribute are probably more useful.

For new contributors, I think a better way to start would be to look at issues and confirm them, investigate the cause of issues (gets you familiar with the codebase), review other people's PRs (helps familiarise oneself with what is expected) - jumping straight to a code contribution is rushing ahead too quickly, and not building the base knowledge/background needed. (edit: I think with how complex this project is, jumping to code contribution is just not realistic for a someone learning, unless you have a mentor on hand. I can understand that the other types of contributions are less glamourous and historically less valued, but I think you need to work at building a foundation first.)

Maybe this suggestion should be added to the 'New contributor' section, and we could reference it here...

I am trying to think of a better way to phrase 'beginner'. Do you think 'new contributor' is not a good description? I don't love the term beginner (though I can't put my finger on why). I find when people are commenting, wanting to work on an issue, they often use the terms 'new to open source', 'learning'.

I have chosen "beginner" over "new contributor" since the latter come with different skill levels and there are very skilled people whom we'd very much like to contribute right away. Feel free to change "beginner" into something else though. I had also considered "new to open source" and like it.

I think it's ok to subsume "I found a bug" and "I'm missing a feature" under "issues" and to leave it underspecified here (especially since it is expanded in the link). This way, people are not specifically encouraged but the information on how to do it is still there.

Maybe we could even make the intentions you mention below behind suggesting these things first explicit:

```suggestion
* :ref:`improve, triage, and investigate issues <bug_triaging>`; this will also make you more familiar with the codebase
```

```suggestion
* :ref:`reviewing other developers' pull requests <code_review>`; this also helps you to familiarise yourself with what is expected of code contributions
```

I actually like the term `new contributors`, and I think the main point the paragraph above brings across is that the other contribution options should be taken into account first, which I like very much (because it is also the way I'm currently approaching the project, and I wouldn't consider myself a "beginner" 😇 ). Encouraging this path more explicitly might actually make it easier for new contributors to find starting points and could lead to higher quality first code contributions (though this assumes that people actually read this carefully (or that AI agents advise their users on this), which is a strong premise 😬)

I like it.

Some points are "reference", "report" and others are "making", "reviewing" and "improving" - I don't know what the grammar thing to reference is, but I think using the same form for all makes things more consistent. But I could not tell you which of the two types is "better".

I think "new contributor" is good because it communicates what you are new at (contributing). With "beginner" we leave it to the imagination of the reader to determine what it refers to (coding? open-source contributions? cooking? reviewing?).

In addition, you can be an experienced programmer/software engineer but still new to contributing to open-source. In which case I think starting somewhere else to learn about contributing is a good idea.

Good point, I made everything '-ing', because it sounded marginally nicer to me, but open to suggestions.

I liked adding these to the bullet list here, but I ultimately thought it would be better to expand the 'New contributor' section, to more explicitly suggest building a foundation first and how to do this.

I've linked the new contributor section at the end of this 'ways to contribute' section.

You remove the button which was showing the number of stars of the repos and where clicking on it would go to https://github.com/[USER_REDACTED]n/scikit-learn/stargazers. I think that's completely fine because it was not very useful. If we really want (in a further PR), we could add a button where you can star easily, looks like GitHub makes your life easier https://buttons.github.io/

```suggestion
We recommend that new contributors first gain foundational knowledge on scikit-learn
```

I don't know how to word this clearly, and this doesn't need to be in this PR, but maybe something like "it may not the best place to begin if you are new to open-source contribution in general"

In my mind there is a difference between:
- new to open-source contribution in general
- new contributor to scikit-learn, but who has already contributed to other open-source projects

Actually, maybe this sentence could be replaced by
```suggestion
To become familiar with the code base, you should to start by:
```
to 1) remove the repetition of the sentence start above, 2) not mention "non-code" explicitly (because I think a slightly negative sentiment towards this still exists).
Also, we already link to `ways to contribute` in the sentence above, and since the following repeats (and elaborates) some of the points given there, I don't think we need the link here.

I think this also captures what [USER_REDACTED] was trying to add, so I think it is worth addressing it in this PR. Maybe that could be a way of phrasing it:

"it may not the best place to begin if you are new to open-source contribution in general. In this case, you should follow the suggestions in..."

Because the new section for new contributors now makes it more explicit, and this one here still sets expectations, even for experienced open-source contributors, but doesn't discourage code contributions in general (at least how I read it).

Ah I didn't realise it went somewhere. This was mostly because I thought the image didn't fit the format of the new bullet points. The problem with stargazers is that the page does not allow you to easily star the project, you just see a list of other users who have starred it.

> we could add a button where you can star easily

as in we can add a button which when clicked, stars the project? Or would it just take you to stargazers?

I've amended to Annes wording.

Essentially if you're new to open source in general, please make non code contributions first. The only snag here is that, would you suggest those that are not new to open source contribution, but are new to scikit-learn also read the new contributor section...? Or should we expect them to work out what they need to read?

Ah yes I caught this repetition too, before I read this, and I changed to something else. Let me know what you think.

Having a re-read, I moved the 'to read' contributing sections that pertain to contributing code down here, as we don't talk about code contributions until here.

I think from the bullet points above this section they should find enough resources on where to start. And no one is stopping them from skimming the new contributors section if they are missing any information.

I think [USER_REDACTED] mentioned some ideas on how to re-structure the page into separate explicit "paths" for different target groups, but I would leave this for a follow-up PR at this stage.


TST Fix test_min_dependencies_readme.py with upper bounds

Noticed when setting upper bounds in the 1.8 release candidate PR in https://github.com/[USER_REDACTED]n/scikit-learn/pull/32766

I think the previous test was not testing anything which is probably why we never noticed see https://github.com/[USER_REDACTED]n/scikit-learn/pull/31656/files#r2334147276 ...

cc [USER_REDACTED]

@@ -96,7 +96,7 @@ build-backend = "mesonpy"
 # Minimum requirements for the build system to execute.
 requires = [
     "meson-python>=0.17.1",
-    "Cython>=3.1.2",
+    "cython>=3.1.2",
     "numpy>=2",
     "scipy>=1.10.0",
 ]

@@ -96,20 +96,19 @@ def check_pyproject_section(
         info = info[key]

     pyproject_build_min_versions = {}
+    # Assuming pyproject.toml build section has something like "my-package>=2.3.0"
+    # Warning: if you try to modify this regex, bear in mind that there can be upper
+    # bounds in release branches so "my-package>=2.3.0,<2.5.0"
+    pattern = r"([\w-]+)\s*[>=]=\s*([\d\w.]+)"
     for requirement in info:
-        if ">=" in requirement:
-            package, version = requirement.split(">=")
-        elif "==" in requirement:
-            package, version = requirement.split("==")
-        else:
+        match = re.search(pattern, requirement)
+        if match is None:
             raise NotImplementedError(
-                f"{requirement} not supported yet in this test. "
+                f"{requirement} does not match expected regex {pattern!r}. "
                 "Only >= and == are supported for version requirements"
             )

-        # [NAME_REDACTED] in pyproject.toml but cython in _min_dependencies.py
-        if package == "Cython":
-            package = "cython"
+        package, version = match.group(1), match.group(2)

         pyproject_build_min_versions[package] = version

Looks good but not actually tested against upper bounded requirements in this PR.
It could have been done in 1.8.X and backported in main, but let's not bother and merge anyway. I believe that you already tested from your release branch.

Thanks, [USER_REDACTED]!

Just a quick question, why did the `.split(">=")` not work before?

```suggestion
    # bounds in release branches so "my-package>=<min_version>,<max_version>"
```

```suggestion
    # Assuming pyproject.toml build section has something like "my-package>=<min_version>"
```
optional nit. The ">=<" is a little hard to read at first, and I was hoping the quotes would help.

Good point that using `<min_version>` is hard to parse with the `>=`, I pushed a commit to use actual numbers which is clearer

I drew inspiration from [USER_REDACTED]'s playbook here of cleaning-up things around the code I modify, nobody complained so I may be trying to do it more often 😅