If you have a skewed dataset for binary classification with 90% positive samples and only 10% negative samples,
you don't want to use random k-fold cross-validation. Using simple k-fold cross-validation for a dataset like this can result in folds with all negative samples.
In these cases, we prefer using stratified k-fold cross-validation.Stratified k-fold cross-validation keeps the ratio of labels in each fold constant. So,in each fold, you will have the same 90% positive and 10% negative samples. Thus,whatever metric you choose to evaluate, it will give similar results across all folds.
stratified k-fold可以避免fold中全是正样本，负样本的问题，保证每个fold中正负 样本 比率相同。
import pandas as pd
from sklearn import model_selection
if __name__ == "__main__":
# Training data is in a csv file called train.csv
df = pd.read_csv("train.csv")
# we create a new column called kfold and fill it with -1
df["kfold"] = -1
# the next step is to randomize the rows of the data
df = df.sample(frac=1).reset_index(drop=True)
# fetch targets
y = df.target.values
# initiate the kfold class from model_selection module
kf = model_selection.StratifiedKFold(n_splits=5)
# fill the new kfold column
for f, (t_, v_) in enumerate(kf.split(X=df, y=y)):
df.loc[v_, 'kfold'] = f
# save the new csv with kfold column
df.to_csv("train_folds.csv", index=False)