Ahh.. nice.. I will use that.. thanks a lot, Sebastian! Best, Raga
On Thu, Jan 26, 2017 at 6:34 PM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > 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 <raga.mark...@gmail.com> > 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 > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
