cross_val_score交叉验证及其用于参数选择、模型选择、特征选择

最新推荐文章于 2025-07-08 13:15:13 发布

starky0729

最新推荐文章于 2025-07-08 13:15:13 发布

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分类专栏：机器学习深度学习文章标签： cross_val_score交叉验证及其用于参数选择、模型选择、特征

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本文深入探讨了sklearn中K折交叉验证(KFold)的使用方法，包括参数解释、属性介绍及实例演示，展示了如何通过调整shuffle和random_state参数影响验证集的划分。

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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]

②设置shuffle=True时，运行两次，发现两次运行的结果不同


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]

③设置shuffle=True和random_state=整数，发现每次运行的结果都相同


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]

④n_splits属性值获取方式


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