Better documentation for random_state

Sort of like https://github.com/scikit-learn/scikit-learn/issues/14228, but for random_state.

For any public object that accepts a random_state parameter, we should document what parts of the algorithm are randomized. It’s not always obvious what is and what isn’t randomized. We should also always link to the glossary, where the different possible values of random_state are clearly explained.

For example for the random forest estimators, it would be helpful to indicate that random_state determines in particular the subsampling of the samples and the subsampling of the features. Something like:

random_state : int, np.random.RandomStateInstance or None, default=None
	Controls the randomness of the estimator, in particular the subsampling
    of the samples and the subsampling of the features. See 
	term:`random_state` for details.

[jnothman]

I think it would be helpful, @nicolashug, to give some examples of what this should look like. Thanks for continuing this work.

[mschaffenroth]

The script from #14228 (see here) adapted for the random_state parameter got the following results:

• sklearn/multioutput.py - 561, 721
• sklearn/multiclass.py - 669
• sklearn/random_projection.py - 170, 229, 447, 567
• sklearn/dummy.py - 52
• sklearn/kernel_approximation.py - 41, 143, 468
• sklearn/inspection/permutation_importance.py - 86
• sklearn/impute/_iterative.py - 124
• sklearn/covariance/robust_covariance.py - 63, 233, 328, 545
• sklearn/covariance/elliptic_envelope.py - 40
• sklearn/utils/testing.py - 566
• sklearn/utils/init.py - 484, 629
• sklearn/utils/random.py - 39
• sklearn/utils/extmath.py - 185, 297
• sklearn/tree/tree.py - 652, 1033, 1320, 1549
• sklearn/neighbors/nca.py - 111
• sklearn/neighbors/kde.py - 209
• sklearn/feature_extraction/image.py - 324, 456
• sklearn/mixture/gaussian_mixture.py - 504
• sklearn/mixture/bayesian_mixture.py - 166
• sklearn/mixture/base.py - 139
• sklearn/metrics/cluster/unsupervised.py - 80
• sklearn/manifold/locally_linear.py - 146, 252, 584
• sklearn/manifold/t_sne.py - 552
• sklearn/manifold/spectral_embedding_.py - 171, 387
• sklearn/manifold/mds.py - 51, 198, 314
• sklearn/neural_network/_multilayer_perceptron.py - 782, 1174
• sklearn/neural_network/_rbm.py - 59
• sklearn/decomposition/fastica_.py - 205, 344
• sklearn/decomposition/nmf.py - 282, 467, 958, 1158
• sklearn/decomposition/online_lda.py - 60, 79, 225
• sklearn/decomposition/kernel_pca.py - 78
• sklearn/decomposition/factor_analysis.py - 90
• sklearn/decomposition/truncated_svd.py - 59
• sklearn/decomposition/dict_learning.py - 364, 485, 692, 1135, 1325
• sklearn/decomposition/sparse_pca.py - 82, 285
• sklearn/decomposition/pca.py - 192
• sklearn/gaussian_process/gpc.py - 110, 527
• sklearn/gaussian_process/gpr.py - 109, 366
• sklearn/preprocessing/data.py - 2138, 2557
• sklearn/feature_selection/mutual_info_.py - 226, 335, 414
• sklearn/model_selection/_split.py - 373, 578, 1080, 1185, 1239, 1379, 1481, 1594, 2036
• sklearn/model_selection/_validation.py - 1006, 1176
• sklearn/model_selection/_search.py - 214, 1301
• sklearn/linear_model/ransac.py - 152
• sklearn/linear_model/perceptron.py - 55
• sklearn/linear_model/theil_sen.py - 243
• sklearn/linear_model/stochastic_gradient.py - 357, 798, 1403
• sklearn/linear_model/logistic.py - 587, 753, 1087, 1262, 1812
• sklearn/linear_model/passive_aggressive.py - 76, 322
• sklearn/linear_model/coordinate_descent.py - 579, 859, 1312, 1486, 1664, 1850, 2013, 2187
• sklearn/linear_model/ridge.py - 324, 692, 846
• sklearn/linear_model/base.py - 65
• sklearn/linear_model/sag.py - 154
• sklearn/svm/classes.py - 90, 310, 544, 750
• sklearn/svm/base.py - 852
• sklearn/datasets/kddcup99.py - 79
• sklearn/datasets/twenty_newsgroups.py - 187
• sklearn/datasets/olivetti_faces.py - 64
• sklearn/datasets/samples_generator.py - 127, 323, 429, 520, 604, 666, 738, 875, 936, 1001, 1077, 1130, 1189, 1229, 1278, 1339, 1391, 1454, 1542, 1633
• sklearn/datasets/rcv1.py - 114
• sklearn/datasets/base.py - 146
• sklearn/datasets/covtype.py - 69
• sklearn/cluster/k_means_.py - 56, 242, 382, 585, 701, 1148, 1366
• sklearn/cluster/spectral.py - 41, 197, 313
• sklearn/cluster/bicluster.py - 235, 379
• sklearn/cluster/mean_shift_.py - 48
• sklearn/cluster/tests/test_k_means.py - 294, 305
• sklearn/ensemble/gradient_boosting.py - 1919, 2402
• sklearn/ensemble/bagging.py - 500, 900
• sklearn/ensemble/weight_boosting.py - 197, 330, 471, 890, 1012
• sklearn/ensemble/iforest.py - 109
• sklearn/ensemble/forest.py - 941, 1256, 1531, 1822, 2051
• sklearn/ensemble/base.py - 56
• sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py - 738, 918
• sklearn/ensemble/_hist_gradient_boosting/binning.py - 37, 112