Scikit-learn: Make random_state descriptions more informative and refer to Glossary

Created on 29 Jan 2018 · 60Comments · Source: scikit-learn/scikit-learn

We recently added a Glossary to our documentation, which describes common parameters among other things. We should now replace descriptions of random_state parameters to make them more concise and informative (see #10415). For example, instead of

    random_state : int, RandomState instance or None, optional, default: None
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

in both KMeans and MiniBatchKMeans, we might have:

KMeans:
    random_state : int, RandomState instance, default=None
        Determines random number generation for centroid initialization.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.


MiniBatchKMeans:
    random_state : int, RandomState instance, default=None
        Determines random number generation for centroid initialization and
        random reassignment.
        Pass an int for reproducible results across multiple function calls.
        See :term:`Glossary <random_state>`.

Therefore, the description should focus on what is the impact of random_state on the algorithm.

Contributors interested in contributing this change should take on one module at a time, initially.

The list of estimators to be modified is the following:

List of files to modify using kwinata script

[x] [sklearn/dummy.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/dummy.py) - 59
[x] [sklearn/multioutput.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/multioutput.py) - 578, 738
[ ] [sklearn/kernel_approximation.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/kernel_approximation.py) - 41, 143, 470
[ ] [sklearn/multiclass.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/multiclass.py) - 687
[x] [sklearn/random_projection.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/random_projection.py) - 178, 245, 464, 586
[x] [sklearn/feature_extraction/image.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/image.py) - 368, 502
[x] [sklearn/utils/random.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/random.py) - 39 open PR
[x] [sklearn/utils/extmath.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py) - 185, 297
[x] [sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py) - 736, 918
[x] [sklearn/ensemble/_hist_gradient_boosting/binning.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_hist_gradient_boosting/binning.py) - 37, 112
[x] [sklearn/ensemble/_bagging.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_bagging.py) - 503, 902
[x] [sklearn/ensemble/_gb.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_gb.py) - 887, 1360
[x] [sklearn/ensemble/_forest.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_forest.py) - 965, 1282, 1559, 1868, 2103
[x] [sklearn/ensemble/_iforest.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_iforest.py) - 109
[ ] [sklearn/ensemble/_base.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_base.py) - 52
[x] [sklearn/ensemble/_weight_boosting.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_weight_boosting.py) - 188, 324, 479, 900, 1022
[x] [sklearn/decomposition/_truncated_svd.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_truncated_svd.py) - 59 Open PR
[x] [sklearn/decomposition/_kernel_pca.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_kernel_pca.py) - 79 Open PR
[x] [sklearn/decomposition/_dict_learning.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_dict_learning.py) - 364, 485, 692, 1135, 1325 Open PR
[x] [sklearn/decomposition/_fastica.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_fastica.py) - 205, 344 Open PR
[x] [sklearn/decomposition/_nmf.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_nmf.py) - 290, 475, 966, 1159 Open PR
[x] [sklearn/decomposition/_pca.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_pca.py) - 192 Open PR
[x] [sklearn/decomposition/_sparse_pca.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_sparse_pca.py) - 82, 285 Open PR
[x] [sklearn/decomposition/_lda.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_lda.py) - 60, 79, 225 Open PR
[x] [sklearn/decomposition/_factor_analysis.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/decomposition/_factor_analysis.py) - 92 Open PR
[x] [sklearn/cluster/_kmeans.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_kmeans.py) - 56, 241, 380, 583, 700, 1150, 1370
[x] [sklearn/cluster/_spectral.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_spectral.py) - 41, 197, 313
[x] [sklearn/cluster/_bicluster.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_bicluster.py) - 236, 383
[x] [sklearn/cluster/_mean_shift.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/_mean_shift.py) - 48
[x] [sklearn/preprocessing/_data.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_data.py) - 2178, 2607
[x] [sklearn/impute/_iterative.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/impute/_iterative.py) - 125
[x] [sklearn/linear_model/_ransac.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_ransac.py) - 152 Open PR
[x] [sklearn/linear_model/_coordinate_descent.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_coordinate_descent.py) - 580, 860, 1313, 1487, 1665, 1851, 2016, 2192 Open PR
[x] [sklearn/linear_model/_sag.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_sag.py) - 154 Open PR
[x] [sklearn/linear_model/_perceptron.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_perceptron.py) - 55 Open PR
[x] [sklearn/linear_model/_passive_aggressive.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_passive_aggressive.py) - 76, 322 Open PR
[x] [sklearn/linear_model/_logistic.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_logistic.py) - 587, 924, 1100, 1658 Open PR
[x] [sklearn/linear_model/_base.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_base.py) - 65
[x] [sklearn/linear_model/_stochastic_gradient.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_stochastic_gradient.py) - 369, 811, 1419 Open PR
[x] [sklearn/linear_model/_theil_sen.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_theil_sen.py) - 243 Open PR
[x] [sklearn/linear_model/_ridge.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/_ridge.py) - 325, 693, 853 Open PR
[x] [sklearn/tree/_classes.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_classes.py) - 653, 1033, 1322, 1552
[x] [sklearn/feature_selection/_mutual_info.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_selection/_mutual_info.py) - 226, 335, 414
[x] [sklearn/metrics/cluster/_unsupervised.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/cluster/_unsupervised.py) - 80
[x] [sklearn/svm/_classes.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/_classes.py) - 90, 312, 546, 752 Open PR
[x] [sklearn/svm/_base.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/_base.py) - 853 Open PR
[x] [sklearn/inspection/_permutation_importance.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/inspection/_permutation_importance.py) - 81
[x] [sklearn/gaussian_process/_gpr.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/gaussian_process/_gpr.py) - 109, 382
[x] [sklearn/gaussian_process/_gpc.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/gaussian_process/_gpc.py) - 110, 537
[x] [sklearn/manifold/_spectral_embedding.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/_spectral_embedding.py) - 171, 387
[x] [sklearn/manifold/_locally_linear.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/_locally_linear.py) - 146, 252, 584
[x] [sklearn/manifold/_t_sne.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/_t_sne.py) - 558
[x] [sklearn/manifold/_mds.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/manifold/_mds.py) - 51, 198, 314
[x] [sklearn/utils/_testing.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/_testing.py) - 521
[x] [sklearn/utils/__init__.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/__init__.py) - 478, 623
[x] [sklearn/datasets/_kddcup99.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_kddcup99.py) - 79
[x] [sklearn/datasets/_covtype.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_covtype.py) - 69
[x] [sklearn/datasets/_rcv1.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_rcv1.py) - 114
[x] [sklearn/datasets/_samples_generator.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_samples_generator.py) - 127, 323, 440, 531, 618, 688, 767, 904, 965, 1030, 1106, 1159, 1218, 1258, 1307, 1368, 1420, 1483, 1571, 1662
[x] [sklearn/datasets/_olivetti_faces.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_olivetti_faces.py) - 64
[x] [sklearn/datasets/_base.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_base.py) - 157
[x] [sklearn/datasets/_twenty_newsgroups.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/datasets/_twenty_newsgroups.py) - 187
[x] [sklearn/mixture/_bayesian_mixture.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/_bayesian_mixture.py) - 166
[x] [sklearn/mixture/_base.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/_base.py) - 139
[x] [sklearn/mixture/_gaussian_mixture.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/mixture/_gaussian_mixture.py) - 504
[x] [sklearn/model_selection/_validation.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_validation.py) - 1006, 1176
[x] [sklearn/model_selection/_split.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_split.py) - 382, 588, 1091, 1196, 1250, 1390, 1492, 1605, 2049 Open PR
[x] [sklearn/model_selection/_search.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/model_selection/_search.py) - 207, 1299
[x] [sklearn/neural_network/_multilayer_perceptron.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neural_network/_multilayer_perceptron.py) - 782, 1174
[x] [sklearn/neural_network/_rbm.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neural_network/_rbm.py) - 59
[x] [sklearn/neighbors/_kde.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/_kde.py) - 233
[x] [sklearn/neighbors/_nca.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/_nca.py) - 112
[x] [sklearn/covariance/_robust_covariance.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_robust_covariance.py) - 63, 233, 328, 545
[x] [sklearn/covariance/_elliptic_envelope.py](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/covariance/_elliptic_envelope.py) - 40

Documentation Moderate Sprint good first issue

Source

jnothman

Most helpful comment

We want to work on sklearn/preprocessing/_data.py - 2178, 2607
@rachelcjordan and @fabi-cast

wimlds #SciKitLearnSprint

Olks on 25 Jan 2020

👍2

All 60 comments

Hi @jnothman, Can I take this issue? Thanks

aby0 on 29 Jan 2018

Claim a module/subpackage and have a go...

On 30 January 2018 at 00:24, Somya Anand notifications@github.com wrote:

Hi @jnothman https://github.com/jnothman, Can I take this issue? Thanks

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jnothman on 29 Jan 2018

@jnothman I am sorry for being naive but can you elaborate about the module/submodule? I mean are you referring to a sub-package like Kmeans for instance?

aby0 on 29 Jan 2018

I think what @jnothman means is just start with one file, for example sklearn/cluster/k_means_.py, update the random_state docstring as in the top post and open a PR.

lesteve on 29 Jan 2018

a subpackage is something like sklearn.cluster

jnothman on 29 Jan 2018

Thanks. Will do that and open a PR.

aby0 on 30 Jan 2018

Hi! @jnothman

Would you also like to replace the following comments as seen in grid_search.py? They have an extra line as compared to the one shared by you.

random_state : int, RandomState instance or None, optional (default=None)
        Pseudo random number generator state used for random uniform sampling
        from lists of possible values instead of scipy.stats distributions.
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

shivamriky on 30 Jan 2018

I can take grid_search.py and k_means.py(KMeans).

shivamriky on 30 Jan 2018

leave grid_search.py alone. it is deprecated. The idea is to minimise the
content that is repeated, and available in the glossary, so that we can
give the users to most informative description about random_state's role in
the particular estimator.

jnothman on 30 Jan 2018

Thanks @jnothman. WIll I need to understand these algorithms before I can replace this random_state information?

shivamriky on 31 Jan 2018

You will need to understand the algorithms broadly, but not every detail of
their implementation. You will need to be able to find where random_state
is used, if the randomisation in the algorithm is not completely obvious.
In some cases, it may be appropriate to not even give much more detail than
just linking to the glossary; we'll have to see how it goes.

jnothman on 31 Jan 2018

Okay, thank you. I will start going through the algorithms slowly.

Regards,
Shivam Rastogi

shivamriky on 3 Feb 2018

I have opened a pull request #10614

shivamriky on 10 Feb 2018

Since @aby0 has not claimed the sklearn.cluster module yet. I would like to claim the whole module. Please let me know if I can work on it or I should work on something else.

shivamriky on 10 Feb 2018

Any update guys? It is a long holiday for us so let me know if I can pick this.

shivamriky on 15 Feb 2018

I'll take the datasets module since I'm already poking around in the docstrings there for #10731.

richford on 28 Feb 2018

I'm claiming the linear_model module. ~~will raise a PR soon.~~ #11900 raised.

Claiming decomposition module next.

SirR4T on 23 Aug 2018

Checklist of modules where this needs to be done:

[ ] developers
[ ] covariance
[x] decomposition
[ ] dummy.py
[ ] ensemble
[ ] feature_extraction
[ ] feature_selection
[ ] gaussian_process
[ ] kernel_approximation.py
[x] linear_model via #11900
[ ] manifold
[ ] metrics
[ ] mixture
[ ] model_selection
[ ] multiclass.py
[ ] multioutput.py
[ ] neighbors
[ ] neural_network
[ ] preprocessing
[ ] random_projection.py
[ ] svm
[ ] tree
[ ] utils

SirR4T on 23 Aug 2018

We had some trouble reaching consensus on how to strike the right balance
here, iirc

jnothman on 23 Aug 2018

So do pay attention to the prior PRs merged above

jnothman on 23 Aug 2018

@jnothman thanks! will update the PRs for to mention the reproducibility when passing an int.

SirR4T on 23 Aug 2018

willing to take up all the other modules in another PR, once these have been reviewed...

SirR4T on 25 Aug 2018

I'm claiming covariance.

BlackTeaAndCoffee on 1 Sep 2018

@BlackTeaAndCoffee please be aware, the doc string format is not yet finalised, discussions have been happening on the other PRs listed here. So you might wanna have a look too.

SirR4T on 1 Sep 2018

I am claiming feature_extraction

erodrago on 22 Jun 2019

@jnothman , @NicolasHug, just discovered #15222 and a number of PR related to it that I haven't taken into account in summarizing this one... some of them are never been reviewed... :(
In order to make things clear for sprints, I'm wondering if we can close one of those two issues: if yes, which one? As I can avoid duplicated information. Thanks for your collaboration.

cmarmo on 8 Jan 2020

I wasn't aware of this issue (should have checked better), I'm happy to close https://github.com/scikit-learn/scikit-learn/issues/15222 in favor of this one

NicolasHug on 8 Jan 2020

Following @jnothman comment maybe this issue could deserve a 'Moderate' label?

cmarmo on 9 Jan 2020

We want to work on ensemble/_hist_gradient_boosting/binning.
@mojc and me.

wimlds

Malesche on 25 Jan 2020

@anaisabeldhero and me want to work on manifold/*
#wimlds #SciKitLearnSprint

daphn3k on 25 Jan 2020

@daphn3k and I will work on sklearn/gaussian_process/

wimlds #SciKitLearnSprint

anaisabeldhero on 25 Jan 2020

We want to work on sklearn/preprocessing/_data.py - 2178, 2607
@rachelcjordan and @fabi-cast

wimlds #SciKitLearnSprint

Olks on 25 Jan 2020

👍2

Me and @Malesche want to take the sklearn/inspection/_permutation_importance.py

WiMLDS

mojc on 25 Jan 2020

👍1

claiming sklearn/metrics/cluster/_unsupervised.py file! #wimlds

stareh on 25 Jan 2020

@daphn3k and I take also the covariance/* and neighbors/* #wimlds

anaisabeldhero on 25 Jan 2020

claim:
sklearn/dummy.py - 59
sklearn/multioutput.py - 578, 738
sklearn/kernel_approximation.py - 41, 143, 470
sklearn/multiclass.py - 687
sklearn/random_projection.py - 178, 245, 464, 586

stareh on 25 Jan 2020

PSA: please use the original sentence

Pass an int for reproducible results across multiple function calls.

instead of what I'm seeing in PRs at the moment:

Use an int to make the randomness deterministic

which isn't correct, since the RNG is always deterministic regardless of what is passed

CC @adrinjalali since I think you're at the sprint

NicolasHug on 25 Jan 2020

👍1

working on the neural network and mixture

anaisabeldhero on 25 Jan 2020

PSA: please use the original sentence

Pass an int for reproducible results across multiple function calls.

instead of what I'm seeing in PRs at the moment:

Use an int to make the randomness deterministic

which isn't correct, since the RNG is always deterministic regardless of what is passed

CC @adrinjalali since I think you're at the sprint

Hi @NicolasHug this was meant to comment a PR I suppose... which one? :)

cmarmo on 25 Jan 2020

going to work on scikit-learn/sklearn/model_selection/_validation.py

rachelcjordan on 25 Jan 2020

@cmarmo That was a general comment for all PRs. I saw one and commented there, then saw a second one and figured out it was a pattern that would be better addressed at the source

NicolasHug on 25 Jan 2020

@cmarmo That was a general comment for all PRs. I saw one and commented there, then saw a second one and figured out it was a pattern that would be better addressed at the source

Sorry @NicolasHug, my bad, I haven't found the comment easy to trace.

cmarmo on 25 Jan 2020

@NicolasHug Original sentence has been corrected in the commits from @anaisabeldhero and me

daphn3k on 25 Jan 2020

Me and @Olks claim sklearn/utils/extmath.py - 185, 297

fabi-cast on 25 Jan 2020

Claim sklearn/ensemble/_iforest.py - 109

SergioDSR on 28 Jan 2020

Claim sklearn/neural_network/_multilayer_perceptron.py - 782, 1174

GregoireMialon on 28 Jan 2020

Claim sklearn/ensemble/_weight_boosting.py - 188, 324, 479, 900, 1022

DatenBiene on 29 Jan 2020

Claim sklearn/multioutput.py - 578, 738

DatenBiene on 29 Jan 2020

Claim :
sklearn/mixture/_bayesian_mixture.py - 166
sklearn/mixture/_base.py - 139
sklearn/mixture/_gaussian_mixture.py - 504

DatenBiene on 30 Jan 2020

Claim sklearn/ensemble/_gb.py - 887, 1360

DatenBiene on 30 Jan 2020

Claim sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py - 736, 918

DatenBiene on 30 Jan 2020

Claim sklearn/neural_network/_rbm.py - 59

GregoireMialon on 30 Jan 2020

Claim :

sklearn/svm/_classes.py - 90, 312, 546, 752
sklearn/svm/_base.py - 853

DatenBiene on 30 Jan 2020

Claim:

sklearn/feature_selection/_mutual_info.py - 226, 335, 414
sklearn/metrics/cluster/_unsupervised.py - 80
sklearn/utils/_testing.py - 521
sklearn/utils/init.py - 478, 623

DatenBiene on 30 Jan 2020

Claim :

sklearn/dummy.py - 59
sklearn/random_projection.py - 178, 245, 464, 586

DatenBiene on 31 Jan 2020

@DatenBiene @GregoireMialon Thanks for all your contributions during last sprint. There are only 3 modules left unchecked !

Would you be interested / have time / have motivation to tackle those (no pressure !) ?

jeremiedbb on 12 Feb 2020

Hi Jérémie ! I'll try to have a look at it soon

Le mer. 12 févr. 2020 à 15:53, Jérémie du Boisberranger <
[email protected]> a écrit :

@DatenBiene https://github.com/DatenBiene @GregoireMialon
https://github.com/GregoireMialon Thanks for all your contributions
during last sprint. There are only 3 modules left unchecked !

Would you be interested / have time / have motivation to tackle those (no
pressure !) ?

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GregoireMialon on 13 Feb 2020

Hi @jeremiedbb! I will try to finish the 3 remaining modules today 😃

Claim:

sklearn/kernel_approximation.py - 41, 143, 470
sklearn/multiclass.py - 687
sklearn/ensemble/_base.py - 52

DatenBiene on 4 Apr 2020

Hi @jnothman and @jeremiedbb, looks like all the files where modified. I would be happy to help if you find any remaining issues.

DatenBiene on 16 Apr 2020

👍1

Thanks a lot @DatenBiene and all the contributors that worked to close this issue!
I think we can close this huge one!
Feel free to open new specific issues if something is still missing about random_state description.

cmarmo on 16 Apr 2020

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