Could it also be accounting for +- ? Standard deviation is one sided right?
On Thu, Feb 5, 2015 at 4:54 PM, Joel Nothman <joel.noth...@gmail.com> wrote:
> With cv=5, only the training sets should overlap. Is this adjustment still
> appropriate?
>
> On 6 February 2015 at 06:44, Michael Eickenberg <
> michael.eickenb...@gmail.com> wrote:
>
>> this is most probably due to the fact that 2 = sqrt(5 - 1), a correction
>> to variance reduction incurred by the overlapping nature of the folds. the
>> bootstrap book contains more info on how to calculate these for different
>> cases of splitting.
>>
>> hth,
>> michael
>>
>>
>> On Thursday, February 5, 2015, Sebastian Raschka <se.rasc...@gmail.com>
>> wrote:
>>
>>> Hi,
>>>
>>> I am wondering why the standard deviation of the accuracy estimate is
>>> multiplied by 2 in the example on
>>> http://scikit-learn.org/stable/modules/cross_validation.html; it would
>>> be nice if someone could explain it to me.
>>>
>>> The relevant excerpt from the page linked above:
>>>
>>> >>> clf = svm.SVC(kernel='linear', C=1)
>>> >>> scores = cross_validation.cross_val_score(
>>> ... clf, iris.data, iris.target, cv=5)
>>> ...
>>> >>> scores
>>> array([ 0.96..., 1. ..., 0.96..., 0.96..., 1. ])
>>> The mean score and the standard deviation of the score estimate are
>>> hence given by:
>>> >>>
>>> >>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() *
>>> 2))
>>> Accuracy: 0.98 (+/- 0.03)
>>>
>>>
>>> Best,
>>> Sebastian
>>>
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>>
>>
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>
>
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