Python实现K折交叉验证法的方法步骤

##一个简单的2折交叉验证
from sklearn.model_selection import KFold
import numpy as np
X=np.array([[1,2],[3,4],[1,3],[3,5]])
Y=np.array([1,2,3,4])
KF=KFold(n_splits=2) #建立4折交叉验证方法 查一下KFold函数的参数
for train_index,test_index in KF.split(X):
  print("TRAIN:",train_index,"TEST:",test_index)
  X_train,X_test=X[train_index],X[test_index]
  Y_train,Y_test=Y[train_index],Y[test_index]
  print(X_train,X_test)
  print(Y_train,Y_test)
#小结：KFold这个包 划分k折交叉验证的时候，是以TEST集的顺序为主的，举例来说，如果划分4折交叉验证，那么TEST选取的顺序为[0].[1],[2],[3]。

#提升
import numpy as np
from sklearn.model_selection import KFold
#Sample=np.random.rand(50,15) #建立一个50行12列的随机数组
Sam=np.array(np.random.randn(1000)) #1000个随机数
New_sam=KFold(n_splits=5)
for train_index,test_index in New_sam.split(Sam): #对Sam数据建立5折交叉验证的划分
#for test_index,train_index in New_sam.split(Sam): #默认第一个参数是训练集，第二个参数是测试集
  #print(train_index,test_index)
  Sam_train,Sam_test=Sam[train_index],Sam[test_index]
  print('训练集数量:',Sam_train.shape,'测试集数量:',Sam_test.shape) #结果表明每次划分的数量


#Stratified k-fold 按照百分比划分数据
from sklearn.model_selection import StratifiedKFold
import numpy as np
m=np.array([[1,2],[3,5],[2,4],[5,7],[3,4],[2,7]])
n=np.array([0,0,0,1,1,1])
skf=StratifiedKFold(n_splits=3)
for train_index,test_index in skf.split(m,n):
  print("train",train_index,"test",test_index)
  x_train,x_test=m[train_index],m[test_index]
#Stratified k-fold 按照百分比划分数据
from sklearn.model_selection import StratifiedKFold
import numpy as np
y1=np.array(range(10))
y2=np.array(range(20,30))
y3=np.array(np.random.randn(10))
m=np.append(y1,y2) #生成1000个随机数
m1=np.append(m,y3)
n=[i//10 for i in range(30)] #生成25个重复数据

skf=StratifiedKFold(n_splits=5)
for train_index,test_index in skf.split(m1,n):
  print("train",train_index,"test",test_index)
  x_train,x_test=m1[train_index],m1[test_index]