[scikit-learn] Nested Leave One Subject Out (LOSO) cross validation with scikit
Ludovico Coletta
ludo25_90 at hotmail.com
Wed Dec 7 15:13:28 EST 2016
Thank you for the answer.
I also thought about ShuffleSplit (n_splits=1), but I need to control which indices are used for training and which for testing in the nested folds. The problem is that I did feature selection before hyperparameters optimization (with a nested Leave One Out schema) and now I need the same partitioning for hyperparameters optimization. The reason why I did this is that the feature selection step is incredibly slow, I hope I can get rid of that step in the permutation test. Is not clear to me if I have to include feature selection in the permutation test as well.
Maybe LeavePOut is what I need.
Best
Ludovico
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Da: scikit-learn <scikit-learn-bounces+ludo25_90=hotmail.com at python.org> per conto di scikit-learn-request at python.org <scikit-learn-request at python.org>
Inviato: mercoledì 7 dicembre 2016 17.48
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Oggetto: scikit-learn Digest, Vol 9, Issue 22
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Today's Topics:
1. Re: New to scikit (Andreas Mueller)
2. Re: Nested Leave One Subject Out (LOSO) cross validation with
scikit (Andreas Mueller)
3. return type of StandardScaler (Nilay Shrivastava)
4. Re: return type of StandardScaler (Bharat Didwania .)
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Message: 1
Date: Wed, 7 Dec 2016 11:33:38 -0500
From: Andreas Mueller <t3kcit at gmail.com>
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Subject: Re: [scikit-learn] New to scikit
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http://scikit-learn.org/dev/developers/contributing.html#deprecation
On 12/07/2016 09:42 AM, Chinmay Talegaonkar wrote:
> Yeah, I found an easy bug. Looking for some help in writing
> deprecation cycles for a bug.
>
> On Wed, Dec 7, 2016 at 8:05 PM, Siddharth Gupta
> <siddharthgupta234 at gmail.com <mailto:siddharthgupta234 at gmail.com>> wrote:
>
> Great! Welcome to the community. I would suggest you to check out
> the issues page on the github repo, raise hand to the issues you
> feel like you can give a go to, check out the issues that are
> tagged as require contributor. Issues are a good way to start,
> they will direct you about the areas of the code base to explore.
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> On Dec 7, 2016 6:02 PM, "Chinmay Talegaonkar"
> <chinmay0301 at gmail.com <mailto:chinmay0301 at gmail.com>> wrote:
>
> Hi everyone,
> I have a prior experience in python, and
> have started learning machine learning recently. I wanted to
> contribute to scikit, can anyone suggest a relatively easy
> codebase to explore.
> Thanks in advance!
>
>
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Message: 2
Date: Wed, 7 Dec 2016 11:33:00 -0500
From: Andreas Mueller <t3kcit at gmail.com>
To: Scikit-learn user and developer mailing list
<scikit-learn at python.org>
Subject: Re: [scikit-learn] Nested Leave One Subject Out (LOSO) cross
validation with scikit
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On 12/07/2016 07:41 AM, Ludovico Coletta wrote:
>
> Dear scikit experts,
>
>
> I did as you suggested, but it is not exactly what I would like to do
> ( I also read this:
> http://stackoverflow.com/questions/40400351/nested-cross-validation-with-stratifiedshufflesplit-in-sklearn)
>
> Perhaps I should ask my question in another way: it is possible to
> split the nested cv folds just once? It seems to me that this is not
> possible, do you have any hints?
>
>
Not sure I understand your question.
You can do a single split by using ShuffleSplit(n_splits=1) for example.
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Message: 3
Date: Wed, 7 Dec 2016 22:14:18 +0530
From: Nilay Shrivastava <nilay.euler16 at gmail.com>
To: scikit-learn at python.org
Subject: [scikit-learn] return type of StandardScaler
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StandardScaler returns numpy array even if the object passed is a pandas
dataframe, shouldn't it return a dataframe?
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Message: 4
Date: Wed, 7 Dec 2016 08:48:47 -0800
From: "Bharat Didwania ." <bharat.didwania.eee14 at itbhu.ac.in>
To: Scikit-learn user and developer mailing list
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Subject: Re: [scikit-learn] return type of StandardScaler
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you can use pandas.get_dummies()
<http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html>.
It will perform one hot encoding on categorical columns, and produce a
dataframe as the result. From there you can use pandas.concat([existing_df,
new_df],axis=0) to add the new columns to your existing dataframe. This
will avoid the use of a numpy array.
On Wed, Dec 7, 2016 at 8:44 AM, Nilay Shrivastava <nilay.euler16 at gmail.com>
wrote:
> StandardScaler returns numpy array even if the object passed is a pandas
> dataframe, shouldn't it return a dataframe?
>
>
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