xgboost のstratifiedkfold は使っても意味ないのか

xgboost の training にstratifiedkfoldを使ってみた。

from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score

param_grid = [{'min_child_weight': np.arange(0.1, 10.1, 0.1)}] 

import warnings
warnings.simplefilter('ignore', DeprecationWarning)
kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)
_model = GridSearchCV(XGBClassifier(), param_grid, cv=kf.split(X, y), scoring= 'f1',iid=True)
_model.fit(X, y)
print (model.best_params_)
pred=_model.predict(X)
print('accuracy_score',accuracy_score(y,pred))
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8529741863075196
kf = StratifiedKFold(n_splits=10,random_state=1,shuffle=True)
clf = XGBClassifier(**_model.best_params_)
for i, (train_index,test_index) in enumerate(kf.split(X,y)):
    print('\n{} of kfold {}'.format(i,kf.n_splits))
    xtr,xvl = X.loc[train_index],X.loc[test_index]
    ytr,yvl = y[train_index],y[test_index]
    clf.fit(xtr, ytr,
            eval_set=[(xtr, ytr), (xvl, yvl)],
            eval_metric='logloss',
            verbose=False,
    )
    evals_result = clf.evals_result()
    print (_model.best_params_)
    pred=model.predict(xvl)
    print('accuracy_score',accuracy_score(yvl,pred))
0 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8333333333333334

1 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8666666666666667

2 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8202247191011236

3 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8539325842696629

4 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8314606741573034

5 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.898876404494382

6 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8764044943820225

7 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8314606741573034

8 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8764044943820225

9 of kfold 10
{'min_child_weight': 3.5000000000000004}
accuracy_score 0.8409090909090909