`FutureWarning`s in the documentation

### Describe the issue linked to the documentation

Some `FutureWarning`s are present in the dev documentation and need to be fixed.
Here is a list:

 - [x] [bicluster/plot_bicluster_newsgroups.html](https://scikit-learn.org/dev/auto_examples/bicluster/plot_bicluster_newsgroups.html)
 - [x] [cluster/plot_bisect_kmeans.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_bisect_kmeans.html) #24891
 - [x] [decomposition/plot_ica_blind_source_separation.html](https://scikit-learn.org/dev/auto_examples/decomposition/plot_ica_blind_source_separation.html) #24908
 - [x] [ensemble/plot_gradient_boosting_categorical.html](https://scikit-learn.org/dev/auto_examples/ensemble/plot_gradient_boosting_categorical.html) #24902
 - [x] [cluster/plot_color_quantization.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_color_quantization.html) #24893
 - [x] [ensemble/plot_stack_predictors.html](https://scikit-learn.org/dev/auto_examples/ensemble/plot_stack_predictors.html) #24918
 - [x] [inspection/plot_linear_model_coefficient_interpretation.html](https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html) #24920
 - [x] [~~cross_decomposition/plot_compare_cross_decomposition.html~~](https://scikit-learn.org/dev/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html)
 - [x] [cluster/plot_cluster_comparison.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_cluster_comparison.html) #24927
 - [x] [model_selection/plot_successive_halving_heatmap.html](https://scikit-learn.org/dev/auto_examples/model_selection/plot_successive_halving_heatmap.html) #24968
 - [x] [manifold/plot_compare_methods.html](https://scikit-learn.org/dev/auto_examples/manifold/plot_compare_methods.html) #24909
 - [x] [cluster/plot_kmeans_assumptions.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_kmeans_assumptions.html) #24928
 - [x] [decomposition/plot_faces_decomposition.html](https://scikit-learn.org/dev/auto_examples/decomposition/plot_faces_decomposition.html) #24936
 - [x] [ensemble/plot_gradient_boosting_oob.html](https://scikit-learn.org/dev/auto_examples/ensemble/plot_gradient_boosting_oob.html) #24948
 - [x] [ensemble/plot_gradient_boosting_regression.html](https://scikit-learn.org/dev/auto_examples/ensemble/plot_gradient_boosting_regression.html) #24946
 - [x] [ensemble/plot_gradient_boosting_regularization.html](https://scikit-learn.org/dev/auto_examples/ensemble/plot_gradient_boosting_regularization.html) #24960
 - [x] [manifold/plot_manifold_sphere.html](https://scikit-learn.org/dev/auto_examples/manifold/plot_manifold_sphere.html) #24941
 - [x] [manifold/plot_lle_digits.html](https://scikit-learn.org/dev/auto_examples/manifold/plot_lle_digits.html) #24922
 - [x] [manifold/plot_mds.html](https://scikit-learn.org/dev/auto_examples/manifold/plot_mds.html) #24940
 - [x] [cluster/plot_dict_face_patches.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_dict_face_patches.html) #24910
 - [x] [release_highlights/plot_release_highlights_0_23_0.html](https://scikit-learn.org/dev/auto_examples/release_highlights/plot_release_highlights_0_23_0.html) #24937
 - [x] [release_highlights/plot_release_highlights_1_1_0.html](https://scikit-learn.org/dev/auto_examples/release_highlights/plot_release_highlights_1_1_0.html) #24939
 - [x] ~~[model_selection/plot_grid_search_text_feature_extraction.html](https://scikit-learn.org/dev/auto_examples/model_selection/plot_grid_search_text_feature_extraction.html)~~ https://github.com/scikit-learn/scikit-learn/pull/24961
 - [x] [cluster/plot_kmeans_silhouette_analysis.html](https://scikit-learn.org/dev/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) #24929
 - [x] [applications/plot_cyclical_feature_engineering.html](https://scikit-learn.org/dev/auto_examples/applications/plot_cyclical_feature_engineering.html) #24892

Contributors willing to address this issue, please offer one example per pull request.

Thanks for your help!

### Suggest a potential alternative/fix

_No response_

COMMENT:
> Sorry, this is my first time contributing to sci-learn. When you say fix, should we make it so the future warnings are accurate and change the defaults? Or instead address the TODO's in the code and then remove the warnings?

You have to change the code of each given example so that the warning is no longer raised. In the case of [`cluster/plot_color_quantization.html`](https://scikit-learn.org/dev/auto_examples/cluster/plot_color_quantization.html) the message says
```python-warning
Set the value of `n_init` explicitly to suppress the warning.
```
So the thing to do is to set `n_init=5` explicitly, i.e.
```diff
print("Fitting model on a small sub-sample of the data")
t0 = time()
image_array_sample = shuffle(image_array, random_state=0, n_samples=1_000)
- kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)
+ kmeans = KMeans(n_clusters=n_colors, n_init="auto", random_state=0).fit(image_array_sample)
print(f"done in {time() - t0:0.3f}s.")
```

Labels: Documentation, good first issue, Meta-issue