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:
Here is a non exhaustive list of other examples to be addressed:

- [x] [The Digit Dataset example](https://scikit-learn.org/dev/auto_examples/datasets/plot_digits_last_image.html) can be merged with the [Digit Classification example](https://scikit-learn.org/dev/auto_examples/exercises/plot_digits_classification_exercise.html) or even suppressed. #29931
- [x] [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).
- [ ] [SVM Margins example](https://scikit-learn.org/dev/auto_examples/svm/plot_svm_margin.html) is really similar to [SVM Maximal margin example](https://scikit-learn.org/dev/auto_examples/svm/plot_separating_hyperplane.html) and [Support vectors in Linear SVC](https://scikit-learn.org/dev/auto_examples/svm/plot_linearsvc_support_vectors.html). Either of them could be merged with [SVM separating hyperplane for umbalanced classes](https://scikit-learn.org/dev/auto_examples/svm/plot_separating_hyperplane_unbalanced.html).
- [ ] The [Feature importances with a forest of trees example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html) demonstrates MDI for Random Forests. This is also covered in the [Permutation importance vs MDI example](https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html) and in the [Gradient Boosting Regression example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html).
- [ ] [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) could be merged.
- [x] [Lasso and Elastic Net](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html) can be merged with [Lasso path using Lars example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html). #29955
- [x] [Plot randomly generated classification dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_random_dataset.html) is nice for a visual intuition on the generated datasets, but could as well be replaced by snippets in the respective docstrings. #29937
- [ ] [Plot Ridge coefficients as a function of the regularization](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html) can be merged with [Ridge coefficients as a function of the L2 Regularization](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html).
- [ ] [Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples](https://scikit-learn.org/stable/auto_examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.html) can be removed and the content put in the docstring example section. (edit by @lorentzenchr)
- [ ] [Gradient Boosting Out-of-Bag estimates](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html) and [OOB Errors for Random Forests](https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html) can be merged together (edit by @lorentzenchr)
- [ ] [Two-class AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html) and [Multi-class AdaBoosted Decision Trees](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html) can be merged together (edit by @lorentzenchr)

Please submit one PR per case.

Labels: Documentation, RFC