K折交叉验证：sklearn.model_selection.KFold(n_splits=3, shuffle=False, random_state=None)
思路：将训练/测试数据集划分n_splits个互斥子集，每次用其中一个子集当作验证集，剩下的n_splits-1个作为训练集，进行n_splits次训练和测试，得到n_splits个结果
注意点：对于不能均等份的数据集，其前n_samples % n_splits子集拥有n_samples // n_splits + 1个样本，其余子集都只有n_samples // n_splits样本
参数说明：
n_splits：表示划分几等份
shuffle：在每次划分时，是否进行洗牌
①若为Falses时，其效果等同于random_state等于整数，每次划分的结果相同
②若为True时，每次划分的结果都不一样，表示经过洗牌，随机取样的
random_state：随机种子数
属性：
①get_n_splits(X=None, y=None, groups=None)：获取参数n_splits的值
②split(X, y=None, groups=None)：将数据集划分成训练集和测试集，返回索引生成器
通过一个不能均等划分的栗子，设置不同参数值，观察其结果
①设置shuffle=False，运行两次，发现两次结果相同
-
In [
1]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
False)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
3
4
5
6
7
8
9
10
11] , test_index: [
0
1
2]
-
train_index:[
0
1
2
6
7
8
9
10
11] , test_index: [
3
4
5]
-
train_index:[
0
1
2
3
4
5
8
9
10
11] , test_index: [
6
7]
-
train_index:[
0
1
2
3
4
5
6
7
10
11] , test_index: [
8
9]
-
train_index:[
0
1
2
3
4
5
6
7
8
9] , test_index: [
10
11]
-
-
In [
2]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
False)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
3
4
5
6
7
8
9
10
11] , test_index: [
0
1
2]
-
train_index:[
0
1
2
6
7
8
9
10
11] , test_index: [
3
4
5]
-
train_index:[
0
1
2
3
4
5
8
9
10
11] , test_index: [
6
7]
-
train_index:[
0
1
2
3
4
5
6
7
10
11] , test_index: [
8
9]
-
train_index:[
0
1
2
3
4
5
6
7
8
9] , test_index: [
10
11]
-
In [
3]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
True)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
0
1
2
4
5
6
7
8
10] , test_index: [
3
9
11]
-
train_index:[
0
1
2
3
4
5
9
10
11] , test_index: [
6
7
8]
-
train_index:[
2
3
4
5
6
7
8
9
10
11] , test_index: [
0
1]
-
train_index:[
0
1
3
4
5
6
7
8
9
11] , test_index: [
2
10]
-
train_index:[
0
1
2
3
6
7
8
9
10
11] , test_index: [
4
5]
-
-
In [
4]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
True)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
0
1
2
3
4
5
7
8
11] , test_index: [
6
9
10]
-
train_index:[
2
3
4
5
6
8
9
10
11] , test_index: [
0
1
7]
-
train_index:[
0
1
3
5
6
7
8
9
10
11] , test_index: [
2
4]
-
train_index:[
0
1
2
3
4
6
7
9
10
11] , test_index: [
5
8]
-
train_index:[
0
1
2
4
5
6
7
8
9
10] , test_index: [
3
11]
-
In [
5]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
True,random_state=
0)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
0
1
2
3
5
7
8
9
10] , test_index: [
4
6
11]
-
train_index:[
0
1
3
4
5
6
7
9
11] , test_index: [
2
8
10]
-
train_index:[
0
2
3
4
5
6
8
9
10
11] , test_index: [
1
7]
-
train_index:[
0
1
2
4
5
6
7
8
10
11] , test_index: [
3
9]
-
train_index:[
1
2
3
4
6
7
8
9
10
11] , test_index: [
0
5]
-
-
In [
6]:
from sklearn.model_selection
import KFold
-
...:
import numpy
as np
-
...: X = np.arange(
24).reshape(
12,
2)
-
...: y = np.random.choice([
1,
2],
12,p=[
0.4,
0.6])
-
...: kf = KFold(n_splits=
5,shuffle=
True,random_state=
0)
-
...:
for train_index , test_index
in kf.split(X):
-
...: print(
'train_index:%s , test_index: %s ' %(train_index,test_index))
-
...:
-
...:
-
train_index:[
0
1
2
3
5
7
8
9
10] , test_index: [
4
6
11]
-
train_index:[
0
1
3
4
5
6
7
9
11] , test_index: [
2
8
10]
-
train_index:[
0
2
3
4
5
6
8
9
10
11] , test_index: [
1
7]
-
train_index:[
0
1
2
4
5
6
7
8
10
11] , test_index: [
3
9]
-
train_index:[
1
2
3
4
6
7
8
9
10
11] , test_index: [
0
5]
-
In [
8]: kf.split(X)
-
Out[
8]: <generator object _BaseKFold.split at
0x00000000047FF990>
-
-
In [
9]: kf.get_n_splits()
-
Out[
9]:
5
-
-
In [
10]: kf.n_splits
-
Out[
10]:
5