# Import KFold
from sklearn.model_selection import KFold
# Create a KFold object
kf = KFold(n_splits=5, shuffle=True, random_state=123)
# Loop through each cross-validation split
for train_index, test_index in kf.split(train):
# Get training and testing data for the corresponding split
cv_train, cv_test = train.iloc[train_index], train.iloc[test_index]
層化 K 分割
層化 K 分割
# Import StratifiedKFold
from sklearn.model_selection import StratifiedKFold
# Create a StratifiedKFold object
str_kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=123)
# Loop through each cross-validation split
for train_index, test_index in str_kf.split(train, train['target']):
cv_train, cv_test = train.iloc[train_index], train.iloc[test_index]
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