From joegeisbauer at gmail.com Tue May 4 17:00:07 2021 From: joegeisbauer at gmail.com (Joe Geisbauer) Date: Tue, 4 May 2021 16:00:07 -0500 Subject: [scikit-learn] sklearn-porter support Message-ID: Hello, I was wondering if the sklearn-porter repo was still being maintained. I went today to look when a known issue might be fixed and noticed no work had been performed in over a year on the repo? Is the work waiting on dev resources or are there other concerns keeping work from entering the repo? I would be happy to look into helping with the repo if the concerns are merely developer time. Thank you, Joe Geisbauer From rth.yurchak at gmail.com Tue May 4 17:24:29 2021 From: rth.yurchak at gmail.com (Roman Yurchak) Date: Tue, 4 May 2021 23:24:29 +0200 Subject: [scikit-learn] sklearn-porter support In-Reply-To: References: Message-ID: <77502e11-fe9f-cd4a-0f68-cc6289a9fb7b@gmail.com> Hi Joe, sklearn-porter is a nice project, however people on this mailing list are not really involved with its development. You would likely get more relevant answers to your questions by asking the author directly, for instance in a Github issue. I'm sure they would appreciate an offer to help with the maintenance. Roman On 04/05/2021 23:00, Joe Geisbauer wrote: > Hello, > > I was wondering if the sklearn-porter repo was still being maintained. I went today to look when a known issue might be fixed and noticed no work had been performed in over a year on the repo? Is the work waiting on dev resources or are there other concerns keeping work from entering the repo? I would be happy to look into helping with the repo if the concerns are merely developer time. > > Thank you, > Joe Geisbauer > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > From mitalikatoch at gmail.com Thu May 6 05:51:12 2021 From: mitalikatoch at gmail.com (mitali katoch) Date: Thu, 6 May 2021 11:51:12 +0200 Subject: [scikit-learn] Issue regarding Feature Union Message-ID: Dear Scikit team, I am working with FeatureUnion in the pipeline and best parameters are as follows: Pipeline(steps=[('feature_sel', FeatureUnion(transformer_list= [ ('selectk', SelectKBest(k=500)), ('sel_fromModel', SelectFromModel(estimator=LogisticRegression(C=1, penalty='l1', solver='liblinear'), max_features=100))] )), ('sampler', SMOTE(k_neighbors=2, random_state=10)), ('model', SVC(random_state=10))] ) I would like to extract those SelectKBest(k=500) and max_features=100 from the pipeline. Could you please confirm whether it is possible to do it, If yes, could you share the solution, I would highly appreciate that. Thanks in advance. Best Regards, Mitali Katoch -------------- next part -------------- An HTML attachment was scrubbed... URL: From chris at aridas.eu Thu May 6 06:45:30 2021 From: chris at aridas.eu (Chris Aridas) Date: Thu, 6 May 2021 13:45:30 +0300 Subject: [scikit-learn] Issue regarding Feature Union In-Reply-To: References: Message-ID: Hi, Assuming that you have trained your pipeline, the following piece of code should work. pipeline.named_steps["feature_sel"].transform(X) Best, Chris On Thu, May 6, 2021 at 12:52 PM mitali katoch wrote: > Dear Scikit team, > > I am working with FeatureUnion in the pipeline and best parameters are as > follows: > Pipeline(steps=[('feature_sel', > FeatureUnion(transformer_list= [ ('selectk', > SelectKBest(k=500)), > ('sel_fromModel', > > SelectFromModel(estimator=LogisticRegression(C=1, > > penalty='l1', > > solver='liblinear'), > > max_features=100))] > )), > ('sampler', SMOTE(k_neighbors=2, random_state=10)), > ('model', SVC(random_state=10))] > ) > > I would like to extract those SelectKBest(k=500) and max_features=100 from > the pipeline. > > Could you please confirm whether it is possible to do it, If yes, could > you share the solution, I would highly appreciate that. > > Thanks in advance. > > Best Regards, > Mitali Katoch > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From mitalikatoch at gmail.com Thu May 6 07:42:59 2021 From: mitalikatoch at gmail.com (mitali katoch) Date: Thu, 6 May 2021 13:42:59 +0200 Subject: [scikit-learn] Issue regarding Feature Union In-Reply-To: References:

Message-ID: Hi Chris, I forgot to mention that this pipeline I have used within the GridSearchCV. I have done what you suggested early but didn't work, it said: 'GridSearchCV' object has no attribute 'named_steps'. I somehow figured out now Thanks for your help though. Best regards, Mitali Katoch On Thu, May 6, 2021, 12:47 Chris Aridas wrote: > Hi, > > Assuming that you have trained your pipeline, the following piece of code should work. > > > pipeline.named_steps["feature_sel"].transform(X) > > Best, > Chris > > On Thu, May 6, 2021 at 12:52 PM mitali katoch > wrote: > >> Dear Scikit team, >> >> I am working with FeatureUnion in the pipeline and best parameters are as >> follows: >> Pipeline(steps=[('feature_sel', >> FeatureUnion(transformer_list= [ ('selectk', >> SelectKBest(k=500)), >> ('sel_fromModel', >> >> SelectFromModel(estimator=LogisticRegression(C=1, >> >> penalty='l1', >> >> solver='liblinear'), >> >> max_features=100))] >> )), >> ('sampler', SMOTE(k_neighbors=2, random_state=10)), >> ('model', SVC(random_state=10))] >> ) >> >> I would like to extract those SelectKBest(k=500) and max_features=100 >> from the pipeline. >> >> Could you please confirm whether it is possible to do it, If yes, could >> you share the solution, I would highly appreciate that. >> >> Thanks in advance. >> >> Best Regards, >> Mitali Katoch >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn at python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Thu May 6 07:55:03 2021 From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Thu, 6 May 2021 13:55:03 +0200 Subject: [scikit-learn] Issue regarding Feature Union In-Reply-To: References:

Message-ID: you can get the pipeline (with optimized hyperparameters) using grid_search.best_estimator_. Applying the code of Chris on this estimator will work. On Thu, 6 May 2021 at 13:45, mitali katoch wrote: > Hi Chris, > I forgot to mention that this pipeline I have used within the GridSearchCV. > I have done what you suggested early but didn't work, it said: > 'GridSearchCV' object has no attribute 'named_steps'. > > I somehow figured out now > Thanks for your help though. > > Best regards, > Mitali Katoch > > On Thu, May 6, 2021, 12:47 Chris Aridas wrote: > >> Hi, >> >> Assuming that you have trained your pipeline, the following piece of code should work. >> >> >> pipeline.named_steps["feature_sel"].transform(X) >> >> Best, >> Chris >> >> On Thu, May 6, 2021 at 12:52 PM mitali katoch >> wrote: >> >>> Dear Scikit team, >>> >>> I am working with FeatureUnion in the pipeline and best parameters are >>> as follows: >>> Pipeline(steps=[('feature_sel', >>> FeatureUnion(transformer_list= [ ('selectk', >>> SelectKBest(k=500)), >>> ('sel_fromModel', >>> >>> SelectFromModel(estimator=LogisticRegression(C=1, >>> >>> penalty='l1', >>> >>> solver='liblinear'), >>> >>> max_features=100))] >>> )), >>> ('sampler', SMOTE(k_neighbors=2, random_state=10)), >>> ('model', SVC(random_state=10))] >>> ) >>> >>> I would like to extract those SelectKBest(k=500) and max_features=100 >>> from the pipeline. >>> >>> Could you please confirm whether it is possible to do it, If yes, could >>> you share the solution, I would highly appreciate that. >>> >>> Thanks in advance. >>> >>> Best Regards, >>> Mitali Katoch >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn at python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn at python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From caymanag at yahoo.com Sun May 9 11:17:52 2021 From: caymanag at yahoo.com (caymanag at yahoo.com) Date: Sun, 9 May 2021 15:17:52 +0000 (UTC) Subject: [scikit-learn] UNSUBSCRIBE References: <1579682816.384747.1620573472056.ref@mail.yahoo.com> Message-ID: <1579682816.384747.1620573472056@mail.yahoo.com> On Tuesday, May 4, 2021, 05:24:57 PM EDT, Roman Yurchak wrote: Hi Joe, sklearn-porter is a nice project, however people on this mailing list are not really involved with its development. You would likely get more relevant answers to your questions by asking the author directly, for instance in a Github issue. I'm sure they would appreciate an offer to help with the maintenance. Roman On 04/05/2021 23:00, Joe Geisbauer wrote: > Hello, > > I was wondering if the sklearn-porter repo was still being maintained. I went today to look when a known issue might be fixed and noticed no work had been performed in over a year on the repo? Is the work waiting on dev resources or are there other concerns keeping work from entering the repo? I would be happy to look into helping with the repo if the concerns are merely developer time. > > Thank you, > Joe Geisbauer > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn at python.org https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... URL: From solegalli at protonmail.com Mon May 10 09:28:59 2021 From: solegalli at protonmail.com (Sole Galli) Date: Mon, 10 May 2021 13:28:59 +0000 Subject: [scikit-learn] check_estimator _NotAnArray Message-ID: Hello everyone, I am trying to get Feature-engine transformers pass the check_estimator tests and there is one test, that I am not too sure what it is intended for. The transformers fail the check_transformer_data_not_an_array because the input is a _NotAnArray class, and Feature-engine transformers don't like that. What is this check intended for? Is it to ensure compatibility with some other sklearn class? if yes, which ones? I would appreciate any info or links to docs/ issues. Thanks a lot! Sole -------------- next part -------------- An HTML attachment was scrubbed... URL: From solegalli at protonmail.com Wed May 12 04:46:15 2021 From: solegalli at protonmail.com (Sole Galli) Date: Wed, 12 May 2021 08:46:15 +0000 Subject: [scikit-learn] check_estimator _NotAnArray In-Reply-To: References: Message-ID: fyi, just posted a question in stackoverflow: https://stackoverflow.com/questions/67500110/what-is-the-check-transformer-data-not-an-array-test-from-sklearns-check-estima Are there any plans to expand the docs on the check_estimators test? it would be really helpful to have a general idea of why each test is important, and the consequences of failing this or that test. At least it would be useful for me :p Thank you! Sole ??????? Original Message ??????? On Monday, May 10, 2021 3:28 PM, Sole Galli via scikit-learn wrote: > Hello everyone, > > I am trying to get Feature-engine transformers pass the check_estimator tests and there is one test, that I am not too sure what it is intended for. > > The transformers fail the check_transformer_data_not_an_array because the input is a _NotAnArray class, and Feature-engine transformers don't like that. > > What is this check intended for? Is it to ensure compatibility with some other sklearn class? if yes, which ones? > > I would appreciate any info or links to docs/ issues. > > Thanks a lot! > > Sole -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Wed May 12 06:46:17 2021 From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Wed, 12 May 2021 12:46:17 +0200 Subject: [scikit-learn] check_estimator _NotAnArray In-Reply-To: References:

Message-ID: Scikit-learn estimator should validate X and y. This validation will convert the input X and y into a NumPy array to do the numerical operation of the estimator. This check makes sure that passing an array-like (but not a NumPy array) is still working as passing an array. It basically ensure that the validation under the hood happen. Some estimators are indeed not supporting such a protocol (e.g. Vectorizer) and can skip the test. On Wed, 12 May 2021 at 10:48, Sole Galli via scikit-learn < scikit-learn at python.org> wrote: > fyi, just posted a question in stackoverflow: > > > https://stackoverflow.com/questions/67500110/what-is-the-check-transformer-data-not-an-array-test-from-sklearns-check-estima > > Are there any plans to expand the docs on the check_estimators test? > > it would be really helpful to have a general idea of why each test is > important, and the consequences of failing this or that test. At least it > would be useful for me :p > > Thank you! > > Sole > > > > ??????? Original Message ??????? > On Monday, May 10, 2021 3:28 PM, Sole Galli via scikit-learn < > scikit-learn at python.org> wrote: > > Hello everyone, > > I am trying to get Feature-engine transformers pass the check_estimator > tests and there is one test, that I am not too sure what it is intended for. > > The transformers fail the *check_transformer_data_not_an_array *because > the input is a _NotAnArray class, and Feature-engine transformers don't > like that. > > What is this check intended for? Is it to ensure compatibility with some > other sklearn class? if yes, which ones? > > I would appreciate any info or links to docs/ issues. > > Thanks a lot! > > Sole > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From marmochiaskl at gmail.com Thu May 20 04:18:56 2021 From: marmochiaskl at gmail.com (Chiara Marmo) Date: Thu, 20 May 2021 10:18:56 +0200 Subject: [scikit-learn] Monthly meeting May 25th 2021 Message-ID: Dear list, The scikit-learn monthly meeting will take place on **Tuesday** May 25th at 1PM UTC: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=5&day=25&hour=13&min=0&sec=0&p1=195&p2=224&p3=179&p4=136&p5=240 While these meetings are mainly for core-devs to discuss the current topics, non core-devs and other project maintainers are welcome. Feel free to join, using the following link: https://meet.google.com/xhq-yoga-rtf If you plan to attend and you would like to discuss something specific about your contribution please add your name (or github pseudo) in the " Contributors " section, of the public pad: https://hackmd.io/0yokz72CTZSny8y3Re648Q Best Chiara -------------- next part -------------- An HTML attachment was scrubbed... URL: From emanuele.olivetti at gmail.com Sat May 22 03:02:50 2021 From: emanuele.olivetti at gmail.com (Emanuele Olivetti) Date: Sat, 22 May 2021 09:02:50 +0200 Subject: [scikit-learn] custom scorer needs group information: how? Message-ID: Dear scikit-learn Community, I'd like to create a custom scorer to be used with GroupKFold and GridSearchCV. The issue is that I need to use the grouping information also "inside" the custom scorer, to compute the desired metric. How to do that? Here follows a simplified example to explain in detail the issue. Given this basic and common scenario: --- X = y = groups = np.array([0,0,1,1,1,1,2,2,3,3,3,...]) parameters = {'n_estimators': [10,100,1000], 'max_depth': [5,10,15]} gkf = GroupKFold(n_splits=3) clf = GridSearchCV(RandomForestClassifier(), parameters, scoring=my_scorer) --- how to create my_scorer so that it computes, let's say, the "average accuracy across groups"? Meaning that my_scorer should know not only y_true and y_pred but also their grouping structure. In principle, it should be something like in the following snippet, which needs the group information "for the specific slice of data evaluated" (which I call y_groups below)... a piece of information that I don't know how to propagate there: --- def my_score(y_true, y_pred, y_groups): for group in np.unique(y_groups): idx = y_group==group result.append((y_true[idx] == y_pred[idx]).mean()) return np.mean(result) my_scorer = make_scorer(my_score) --- How can I make a custom scorer that uses inside the group information for the specific predictions to be scored? Thanks in advance for your help, Emanuele -------------- next part -------------- An HTML attachment was scrubbed... URL: From alexandre.gramfort at inria.fr Sat May 22 04:26:01 2021 From: alexandre.gramfort at inria.fr (Alexandre Gramfort) Date: Sat, 22 May 2021 10:26:01 +0200 Subject: [scikit-learn] custom scorer needs group information: how? In-Reply-To: References: Message-ID: hi Emanuelle, I would suggest you have a look at https://github.com/scikit-learn/enhancement_proposals/pull/55 it's work in progress though Alex -------------- next part -------------- An HTML attachment was scrubbed... URL: From emanuele.olivetti at gmail.com Sat May 22 13:53:29 2021 From: emanuele.olivetti at gmail.com (Emanuele Olivetti) Date: Sat, 22 May 2021 19:53:29 +0200 Subject: [scikit-learn] custom scorer needs group information: how? In-Reply-To: References: Message-ID: Hi Alex, Thank you for the quick response. That SLEP looks very interesting! Indeed I had the impression that there was no easy way around the issue of automatically passing additional (meta)data to scorers. Irrespective of my issue, I hope the SLEP will get the green light soon. Best, Emanuele On Sat, May 22, 2021 at 10:27 AM Alexandre Gramfort < alexandre.gramfort at inria.fr> wrote: > hi Emanuelle, > > I would suggest you have a look at > https://github.com/scikit-learn/enhancement_proposals/pull/55 > > it's work in progress though > > Alex > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From reshama.stat at gmail.com Sun May 23 10:37:35 2021 From: reshama.stat at gmail.com (Reshama Shaikh) Date: Sun, 23 May 2021 10:37:35 -0400 Subject: [scikit-learn] [Data Umbrella] scikit-learn Latin America sprint (June 2021) Message-ID: Hello, Data Umbrella is organizing a scikit-learn open source sprint, with a focus on * LATIN AMERICA * on June 26, 2021. This scikit-learn sprint is a 4-hour online hands-on "hackathon" where we work on issues in the scikit-learn GitHub repo to get started in contributing to open source in a structured setting. There is 2-3 hours of pre-work and participants will work with a pair programming partner during the sprint. Sprint website: https://latam2021.dataumbrella.org/home Sprint application: https://forms.gle/cxav9eE9ZkiULfbm7 Tweet: https://twitter.com/DataUmbrella/status/1394661734275821573 LinkedIn post: https://www.linkedin.com/feed/update/urn:li:activity:6800434144624070656/ Meetup event: https://www.meetup.com/data-umbrella/events/278298428/ FAQ: Q: If I am not in Latin America, can I still apply and participate? A: Priority will be given to folks in the Latin America region. If there are open spots, we will open it up to other regions. In the meantime, we have resources available for all to get started in contributing to scikit-learn. Check them out here: https://latam2021.dataumbrella.org/about/prep-work Any questions, please email us: data.umbrella.dei at gmail.com Cheers, Reshama --- Reshama Shaikh she/her Blog | Twitter | LinkedIn | GitHub Data Umbrella NYC PyLadies -------------- next part -------------- An HTML attachment was scrubbed... URL: From reshama.stat at gmail.com Sun May 23 10:38:13 2021 From: reshama.stat at gmail.com (Reshama Shaikh) Date: Sun, 23 May 2021 10:38:13 -0400 Subject: [scikit-learn] [Data Umbrella] 3 Components for Reviewing a Pull Request (PR) Message-ID: Hello, Thomas Fan, a core contributor to scikit-learn, will be presenting on "Reviewing a Pull Request." This live webinar is scheduled for Wednesday, June 2 at 6pm EDT. Sign-up info is here: https://www.meetup.com/data-umbrella/events/278045166/ This presentation will be recorded and shared on YouTube about a day after the event. You can look for it here: https://www.youtube.com/c/DataUmbrella/featured Best, Reshama --- Reshama Shaikh she/her Blog | Twitter | LinkedIn | GitHub Data Umbrella NYC PyLadies -------------- next part -------------- An HTML attachment was scrubbed... URL: From ocombescure at gmail.com Wed May 26 15:25:11 2021 From: ocombescure at gmail.com (olivier combescure) Date: Wed, 26 May 2021 21:25:11 +0200 Subject: [scikit-learn] problem importing sklearn in python Message-ID: <6c5f3b9b-bc74-eb19-f7af-c820202c2b9d@gmail.com> hi, i've always that problem importing from sklearn.cluster import KMeans result : Traceback (most recent call last): ? File ".\test_imports.py", line 3, in ??? from sklearn.cluster import KMeans ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\__init__.py", line 82, in ??? from .base import clone ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\base.py", line 17, in ??? from .utils import _IS_32BIT ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\__init__.py", line 23, in ??? from .class_weight import compute_class_weight, compute_sample_weight ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\class_weight.py", line 7, in ??? from .validation import _deprecate_positional_args ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 26, in ??? from .fixes import _object_dtype_isnan, parse_version ? File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 20, in ??? import scipy.stats ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\__init__.py", line 391, in ??? from .stats import * ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\stats.py", line 180, in ??? from . import distributions ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\distributions.py", line 8, in ??? from ._distn_infrastructure import (entropy, rv_discrete, rv_continuous, ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\_distn_infrastructure.py", line 23, in ??? from scipy import optimize ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\__init__.py", line 400, in ??? from .optimize import * ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\optimize.py", line 36, in ??? from ._numdiff import approx_derivative ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\_numdiff.py", line 6, in ??? from scipy.sparse.linalg import LinearOperator ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\__init__.py", line 114, in ??? from .eigen import * ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\__init__.py", line 9, in ??? from .arpack import * ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\arpack\__init__.py", line 20, in ??? from .arpack import * ? File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 42, in ??? from . import _arpack ImportError: DLL load failed while importing _arpack: La proc?dure sp?cifi?e est introuvable. anaconda 64 bit -- L'absence de virus dans ce courrier ?lectronique a ?t? v?rifi?e par le logiciel antivirus Avast. https://www.avast.com/antivirus From mlcnworkshop at gmail.com Sun May 30 05:30:28 2021 From: mlcnworkshop at gmail.com (MLCN Workshop) Date: Sun, 30 May 2021 11:30:28 +0200 Subject: [scikit-learn] Call for papers for the 4th international workshop on machine learning in clinical neuroimaging Message-ID: Dear Colleagues, *Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) which is held virtually on the 27th of September 2021 at MICCAI 2021 in Strasbourg, France. We welcome contributions to novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *25 June 2021*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. Top accepted papers will be invited to submit an extended version to the MELBA journal. For more information, please visit https://mlcnws.com/call-for-papers/*.* Best wishes, The MLCN 2021 steering and organizing committees Christos Davatzikos, Andre Marquand, Jonas Richiardi, Emma Robinson Ahmed Abdulkadir, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers ================================================================================================ Call for Papers The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/), a satellite event of MICCAI (https://miccai202 1 .org ), calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions. In the machine learning track, we seek novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroscientific data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Big data - Spatio-temporal brain data analysis - Structural data analysis - Graph theory and complex network analysis - Longitudinal data analysis - Model stability and interpretability - Model scalability in large neuroimaging datasets - Multi-source data integration and multi-view learning - Multi-site data analysis, from preprocessing to modeling - Domain adaptation, data harmonization, and transfer learning in neuroimaging - Unsupervised methods for stratifying brain disorders - Deep learning in clinical neuroimaging - Model uncertainty in clinical predictions - ? In the *clinical neuroimaging *track*,* the applications of existing machine learning algorithms are evaluated to move towards precision medicine for complex brain disorders. The discovery of biological markers in medicine is an important challenge across different fields and various experimental procedures and designs are used to detect biological signatures that can be utilized for improvement in diagnostic, treatment, or for other beneficial ends. However, for most complex brain disorders, we do not have reliable biomarkers today. The application of advanced machine learning methods may help to reach this goal. Therefore, we invite the community to submit conference contributions on machine learning approaches with the goal to improve our understanding of complex brain disorders, moving the field closer towards precision medicine. Topics of interest include but are not limited to: - Biomarker discovery - Refinement of nosology and diagnostics - Biological validation of clinical syndromes - Treatment outcome prediction - Course prediction - Analysis of wearable sensors - Neurogenetics and brain imaging genetics - Mechanistic modeling - Brain aging - The presentation of clinical neuroimaging databases to stimulate developments in machine learning - ? ------------------------------ Submission Process: The workshop seeks high-quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (see https://miccai2021.org/en/PAPER-SUBMISSION-GUIDELINES.html for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2021. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2021 conference. ------------------------------ MLCN Special Issue at the MELBA journal: This year, we will invite the top accepted papers to submit an extended version of their contribution to the MLCN special issue at the Journal of Machine Learning for Biomedical Imaging (MELBA) . The invited papers will go through an independent review process by the journal. ------------------------------ Best Paper Award: All MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality, novelty, and clarity of contributions. The winner will be announced at the end of the workshop and will receive a 500 USD honorarium. ------------------------------ Important Dates: - Paper submission deadline: June 25, 2021, 11:59 PM Pacific Time - Notification of Acceptance: July 16, 2021 - Camera-ready Submission: July 30, 2021 - Workshop Date: September 27, 2021 -------------- next part -------------- An HTML attachment was scrubbed... URL: