人工智能 StratifiedKFold_分层采样 机器学习

1、基础

StratifiedKFold——执行分层采样
sklearn.model_selection.StratifiedKFold(n_splits=,random_state=,shuffle=)
y:样本集标记序列
n：整数，数据集大小
n_flods：整数k，大于等于2
shuffle：布尔值，是否混洗数据
random_state整数——随机数种子，否则为随机数生成器

split(X[,y,groups])
X：训练数据集(n_samples,n_features)
y：标记信息(n_samples,)
划分数据集为训练集、测试集

2、代码

X=np.array([[1,2,3,4],
        [11,12,13,14],
        [21,22,23,24],
        [31,32,33,34],
        [41,42,43,44],
        [51,52,53,54],
        [61,62,63,64],
        [71,72,73,74]])
 
y=np.array([1,1,0,0,1,1,0,0])
 
# 普通交叉切分
folder=KFold(n_splits=4,shuffle=False)
for train_index,test_index in folder.split(X,y):
    print("Train Index:",train_index)
    print("Test Index:",test_index)
    print("y_train:",y[train_index])
       print("y_test:",y[test_index])
    print("")
 
# 分层采样交叉切分
stratified_folder=StratifiedKFold(n_splits=4,shuffle=False)
for train_index,test_index in stratified_folder.split(X,y):
    print("Stratified Train Index:",train_index)
    print("Stratified Test Index:",test_index)
    print("Stratified y_train:",y[train_index])
    print("Stratified y_test:",y[test_index])
    print("")

3、结果

【out】：

普通交叉切分:
Train Index: [2 3 4 5 6 7]
Test Index: [0 1]
y_train: [0 0 1 1 0 0]
y_test: [1 1]

普通交叉切分:
Train Index: [0 1 4 5 6 7]
Test Index: [2 3]
y_train: [1 1 1 1 0 0]
y_test: [0 0]

普通交叉切分:
Train Index: [0 1 2 3 6 7]
Test Index: [4 5]
y_train: [1 1 0 0 0 0]
y_test: [1 1]

普通交叉切分:
Train Index: [0 1 2 3 4 5]
Test Index: [6 7]
y_train: [1 1 0 0 1 1]
y_test: [0 0]

分层采样交叉切分:
Stratified Train Index: [1 3 4 5 6 7]
Stratified Test Index: [0 2]
Stratified y_train: [1 0 1 1 0 0]
Stratified y_test: [1 0]

分层采样交叉切分:
Stratified Train Index: [0 2 4 5 6 7]
Stratified Test Index: [1 3]
Stratified y_train: [1 0 1 1 0 0]
Stratified y_test: [1 0]

分层采样交叉切分:
Stratified Train Index: [0 1 2 3 5 7]
Stratified Test Index: [4 6]
Stratified y_train: [1 1 0 0 1 0]
Stratified y_test: [1 0]

分层采样交叉切分:
Stratified Train Index: [0 1 2 3 4 6]
Stratified Test Index: [5 7]
Stratified y_train: [1 1 0 0 1 0]
Stratified y_test: [1 0]

4、分析