Hi, Raga,
I think that if GridSearchCV is used for classification, the stratified k-fold
doesn’t do shuffling by default.
Say you do 20 grid search repetitions, you could then do sth like:
from sklearn.model_selection import StratifiedKFold
for i in range(n_reps):
k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=i)
gs = GridSearchCV(..., cv=k_fold)
...
Best,
Sebastian
> On Jan 26, 2017, at 5:39 PM, Raga Markely <[email protected]> wrote:
>
> Hello,
>
> I was trying to do repeated Grid Search CV (20 repeats). I thought that each
> time I call GridSearchCV, the training and test sets separated in different
> splits would be different.
>
> However, I got the same best_params_ and best_scores_ for all 20 repeats. It
> looks like the training and test sets are separated in identical folds in
> each run? Just to clarify, e.g. I have the following data: 0,1,2,3,4. Class 1
> = [0,1,2] and Class 2 = [3,4]. Suppose I call cv = 2. The split is always for
> instance [0,3] [1,2,4] in each repeat, and I couldn't get [1,3] [0,2,4] or
> other combinations.
>
> If I understand correctly, GridSearchCV uses StratifiedKFold when I enter cv
> = integer. The StratifiedKFold command has random state; I wonder if there is
> anyway I can make the the training and test sets randomly separated each time
> I call the GridSearchCV?
>
> Just a note, I used the following classifiers: Logistic Regression, KNN, SVC,
> Kernel SVC, Random Forest, and had the same observation regardless of the
> classifiers.
>
> Thank you very much!
> Raga
>
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