Xgboost_Sratified_K_Fold

from numpy import loadtxt
import xgboost
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# CV model
model = xgboost.XGBClassifier()
kfold = StratifiedKFold(n_splits=10, random_state=7)
results = cross_val_score(model, X, Y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))