Talip7/scikit-learn-issues-multilabel huggingface dataset:
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
@glemaitre as I mentioned also in https://github.com/scikit-learn/scikit-learn/pull/27108#discussion_r1304196027 , I'm not sure how much we should care about the redirections. I'd be totally okay with giving 404, especially since the only way users could get that 404 would be if they changed the URL manually. We don't provide any way for users to see the same page in a different version, and caring about a good redirect is making it really hard to remove examples.

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
Hi @lorentzenchr,

I would like to work on [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering) examples, having reviewed, my observations and proposed changes are as follows:
- [x] [Agglomerative clustering with and without structure](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html) and [Agglomerative clustering with different metrics](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html)​ are essentially similar examples, would like to keep the metrics example as it would seem more useful and covers enough context on working with agglomreative clustering. 
- [x] The example [Feature agglomeration vs. univariate selection](https://scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html) covers additional content on feature selection than the [Feature agglomeration example](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html), so we could remove the latter.
- [x] [K-means Clustering](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html) - There are already multiple comparative examples that show the difference between regular k-means and other variations like bisecting k-means and mini-batch k-means, so this example could be removed. 

Could you advise if these would suffice or there is room to remove more.

(https://github.com/scikit-learn/scikit-learn/pull/29937)

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
These sound good suggestions @saldanhad

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
Hi @adrinjalali, appreciate your feedback, will submit a PR for these suggested changes soon.

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
Working on the example [Plot randomly generated classification dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_random_dataset.html).

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
<del>[ ]  [Train error vs Test error example](https://scikit-learn.org/dev/auto_examples/model_selection/plot_train_error_vs_test_error.html) is really similar to [Plotting Validation Curves example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html).
</del>

<del> I'd like to work on this part, if it's available.</del>

Edit: Merged.

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
@virchan It was merged

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
@glemaitre @ArturoAmorQ I would like to take [Comparison of Calibration of Classifiers](https://scikit-learn.org/dev/auto_examples/calibration/plot_compare_calibration.html) and [Probability Calibration curves](https://scikit-learn.org/dev/auto_examples/calibration/plot_calibration_curve.html). Can I work on PR?

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
Those two examples in particular are pretty nice, not sure if we need to do anything about those ones?

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
@raviteja-ganta maybe you can merge [Comparison of LDA and PCA 2D projection of Iris dataset](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html) and [Incremental PCA](https://scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html), which are quite similar.

Labels: Documentation, RFC
RFC remove some of our examples

TLDR: I think we have too many examples, in particular in
- [clustering](https://scikit-learn.org/stable/auto_examples/index.html#clustering)
- [ensemble](https://scikit-learn.org/stable/auto_examples/index.html#ensemble-methods)
- [generalized-linear-models](https://scikit-learn.org/stable/auto_examples/index.html#generalized-linear-models) This is the worst.
- [model-selection](https://scikit-learn.org/stable/auto_examples/index.html#model-selection)
- [nearest-neighbors](https://scikit-learn.org/stable/auto_examples/index.html#nearest-neighbors)
- [support-vector-machines](https://scikit-learn.org/stable/auto_examples/index.html#support-vector-machines)

On top, several examples don't render a plot in the gallery, e.g. [Release Highlights 1.0.0](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-1-0-0-py).

I would like to discuss how we can improve this situation. My main worry is that, currently, a user does not find what she/he is looking for but gets overwhelmed by the sheer amount of examples.

Edit: See task list in https://github.com/scikit-learn/scikit-learn/issues/27151#issuecomment-2356198357.

COMMENT:
@ArturoAmorQ  Looks like both are different. First one tries to compare LDA with PCA while second one(Incremental PCA) tries to compare between PCA and incremental PCA.

Labels: Documentation, RFC
⚠️ CI failed on Wheel builder ⚠️

**CI failed on [Wheel builder](https://github.com/scikit-learn/scikit-learn/actions/runs/5959205563)** (Aug 24, 2023)

COMMENT:
## CI is no longer failing! ✅

[Successful run](https://github.com/scikit-learn/scikit-learn/actions/runs/5971617389) on Aug 25, 2023

Labels: Needs Triage
⚠️ CI failed on Linux_nogil.pylatest_pip_nogil ⚠️

**CI failed on [Linux_nogil.pylatest_pip_nogil](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58261&view=logs&j=67fbb25f-e417-50be-be55-3b1e9637fce5)** (Aug 24, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-0-500]
- test_pairwise_distances_argkmin[45-float32-parallel_on_Y-cityblock-0-500]

COMMENT:
Duplicate of https://github.com/scikit-learn/scikit-learn/issues/27126

Labels: Needs Triage
Make automatic validation for sklearn.utils.extmath._randomized_eigsh

https://github.com/scikit-learn/scikit-learn/blob/7f9bad99d6e0a3e8ddf92a7e5561245224dab102/sklearn/utils/extmath.py#L482

COMMENT:
As explained in the linked PR, we don't want to add validation for this function.

Labels: Needs Triage
⚠️ CI failed on Linux.pylatest_pip_openblas_pandas ⚠️

**CI failed on [Linux.pylatest_pip_openblas_pandas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58207&view=logs&j=78a0bf4f-79e5-5387-94ec-13e67d216d6e)** (Aug 23, 2023)
- test_pairwise_distances_argkmin[45-float32-parallel_on_X-cityblock-0-500]
- test_pairwise_distances_argkmin[45-float32-parallel_on_Y-cityblock-0-500]

COMMENT:
Duplicate of #27126.

Labels: Needs Triage
DOC Add permalinks to dropdown headers

### Describe the workflow you want to enable

With addition of dropdowns, you can no longer click on them to get a permalink to the header (and manually adding the `#<header>` to the end of the URL will not take you to the header.

Related: https://github.com/scikit-learn/scikit-learn/issues/26617

### Describe your proposed solution

Potential solution in #26872 but I think we want a non-javascript solution.

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
cc @glemaitre

Labels: New Feature, Documentation
DOC Add permalinks to dropdown headers

### Describe the workflow you want to enable

With addition of dropdowns, you can no longer click on them to get a permalink to the header (and manually adding the `#<header>` to the end of the URL will not take you to the header.

Related: https://github.com/scikit-learn/scikit-learn/issues/26617

### Describe your proposed solution

Potential solution in #26872 but I think we want a non-javascript solution.

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
Thanks @lucyleeow for opening the issue

Labels: New Feature, Documentation
⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️

**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58128&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Aug 21, 2023)
- test_pairwise_distances_argkmin[49-float32-parallel_on_X-braycurtis-0-50]
- test_pairwise_distances_argkmin[49-float32-parallel_on_Y-braycurtis-0-50]

COMMENT:
## CI is no longer failing! ✅

[Successful run](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58166&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161) on Aug 22, 2023

Labels: Needs Triage
⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️

**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58128&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Aug 21, 2023)
- test_pairwise_distances_argkmin[49-float32-parallel_on_X-braycurtis-0-50]
- test_pairwise_distances_argkmin[49-float32-parallel_on_Y-braycurtis-0-50]

COMMENT:
Looks related to #27126.

Labels: Needs Triage
⚠️ CI failed on Linux.py38_conda_defaults_openblas ⚠️

**CI failed on [Linux.py38_conda_defaults_openblas](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=58128&view=logs&j=c8afde5f-ef70-5983-62e8-c6b665ad6161)** (Aug 21, 2023)
- test_pairwise_distances_argkmin[49-float32-parallel_on_X-braycurtis-0-50]
- test_pairwise_distances_argkmin[49-float32-parallel_on_Y-braycurtis-0-50]

COMMENT:
Let's close as duplicate to centralize the discussion there.

Labels: Needs Triage