8000 DOC Updated documentation for cv parameter (issue #4533) by christophebourguignat · Pull Request #5184 · scikit-learn/scikit-learn · GitHub
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Merged
merged 1 commit into from
Aug 30, 2015
Merged

DOC Updated documentation for cv parameter (issue #4533) #5184

merged 1 commit into from
Aug 30, 2015

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christophebourguignat
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@jnothman
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Thanks. This is applicable everywhere there is a cv parameter and a classifier can be passed. I suspect @rvraghav93 is right that we should keep it brief and point to centralised documentation, perhaps just the documentation of check_cv (once clarified, if necessary).

@FedericoV
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This is a good improvement over what was there before. The other places where it shows up are:

666 : 1063 : def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
675 : 1225 : def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
696 : 1543 : def permutation_test_score(estimator, X, y, cv=None,
in cross_validation.py

and

2993 : 232 : def validation_curve(estimator, X, y, param_name, param_range, cv=None,
in validation_curve.py

@GaelVaroquaux
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This is useful. Thanks! Merging.

GaelVaroquaux added a commit that referenced this pull request Aug 30, 2015
@GaelVaroquaux
Merge pull request #5184 from christophebourguignat/master
869971f 
DOC Updated documentation for cv parameter (issue #4533)
@GaelVaroquaux GaelVaroquaux merged commit 869971f into scikit-learn:master Aug 30, 2015
@jnothman
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Someone want to copy to those other destinations?

@christophebourguignat
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@jnothman I can do it

@christophebourguignat
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Just proposed a new PR.

A few questions :

  • in function template we say default cv is None, but in documentation default cv is 3. In check_cv() code this is the same (if cv is None: cv = 3), but shall we unify in the doc (or the code) ?
  • shall we also update cv doc in sklearn.calibration.CalibratedClassifierCV ?

@amueller
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amueller commented Sep 8, 2015

@christophebourguignat which one was your PR?
The main reason 8000 for not doing cv = 3 in all the constructors is that we don't want to hard-code that in so many places iirc. Currently the number 3 is only written down in check_cv.
But we don't want our users to have to dig until they find check_cv to find out what that means.
We could change the default in the docs to None if the rest of the docstring is informative enough.

@raghavrv
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Thanks for the PR this will add more consistency to the cv param documentation.

But @jnothman @GaelVaroquaux @amueller didn't we agree upon cv doc as follows? -

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:
          - None, to use the default 3-fold cross-validation,
          - integer, to specify the number of folds.
          - An object to be used as a cross-validation generator.
          - An iterable yielding train/test splits.

        For integer/None inputs, if ``y`` is binary or multiclass,
        :class:`StratifiedKFold` used. If classifier is False or if ``y`` is
        neither binary nor multiclass, :class:`KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

The current version is -

     cv : integer or cross-validation generator, default=3
        If an integer is passed, it is the number of folds.
        A cross-validation generator to use. If int, determines
        Specific cross-validation objects can be passed, see
        the number of folds in StratifiedKFold if estimator is a classifier
        sklearn.cross_validation module for the list of possible objects
        and the target y is binary or multiclass, or the number
        of folds in KFold otherwise.
        Specific cross-validation objects can be passed, see 
        sklearn.cross_validation module for the list of possible objects.

@raghavrv
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If you are okay with the first doc, let me know I'll add a quick PR and merge the change to #4294 too!

@amueller
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It was a net improvement. The first one looks nicer.

@raghavrv raghavrv mentioned this pull request Sep 10, 2015
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@raghavrv
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#5238

@raghavrv raghavrv mentioned this pull request Sep 10, 2015
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6 participants
@christophebourguignat @jnothman @FedericoV @GaelVaroquaux @amueller @raghavrv
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