Python中的sklearn--KFold與StratifiedKFold

KFold划分數據集的原理：根據n_split直接進行划分

StratifiedKFold划分數據集的原理：划分后的訓練集和驗證集中類別分布盡量和原數據集一樣

 

#導入相關packages

from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold

import numpy as np
from sklearn.model_selection import StratifiedKFold
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([0, 0, 1, 1])
skf = StratifiedKFold(n_splits=2)
skf.get_n_splits(X, y)
>>>2
print(skf)
StratifiedKFold(n_splits=2, random_state=None, shuffle=False)
for train_index, test_index in skf.split(X, y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]    #返回數據框的方法 X_train = X.iloc[train_index]
    y_train, y_test = y[train_index], y[test_index]
>>>TRAIN: [1 3] TEST: [0 2] #返回的是索引
>>>TRAIN: [0 2] TEST: [1 3]