机器学习-KFold交叉验证

Section I: Brief Introduction on StratifiedKFold
A slight improvement over the standard k-fold cross-validation approach is stratified k-fold cross-validattion, which can yeild better bias and variance estimates, especially in case of unequal class proportions. In stratified cross-validattion, the class proportionss are preserved in each fold to ensure that each fold is representative of the class proportions in the training dataset.

FROM
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

Section II: Code and Analyses
代码

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
import numpy as np
from sklearn.model_selection import StratifiedKFold
import warnings
warnings.filterwarnings("ignore")

#Section 1: Load Breast data, i.e., Benign and Malignant
breast=datasets.load_breast_cancer()
X=breast.data
y=breast.target
X_train,X_test,y_train,y_test=\
    train_test_split(X,y,test_size=0.2,stratify=y,random_state=1)

#Section 2: Define PipeLine Model
pipe_lr=make_pipeline(StandardScaler(),
                      PCA(n_components=2),
                      LogisticRegression(random_state=1))

#Section 3: Define StratifiedKFold Model
print("Original Class Dist: %s\n" % np.bincount(y))
kfold=StratifiedKFold(n_splits=10,random_state=1).split(X_train,y_train)
scores=[]
for k,(train_idx,test_idx) in enumerate(kfold):
    pipe_lr.fit(X_train[train_idx],y_train[train_idx])
    score=pipe_lr.score(X_train[test_idx],y_train[test_idx])
    scores.append(score)
    print("Fold: %2d, Class dist: %s, Acc: %.3f" % (k+1,np.bincount(y_train[train_idx]),score))

print('CV Accuracy: %.3f +/- %.3f' % (np.mean(scores),np.std(scores)))

#Section 4: The easier manner when cross_val_score used
from sklearn.model_selection import cross_val_score

scores=cross_val_score(estimator=pipe_lr,
                       X=X_train,
                       y=y_train,
                       cv=10,
                       n_jobs=1)
print("\nCV Accuracy Scores: %s" % scores)
print("CV Accuracy: %.3f +/- %.3f" % (np.mean(scores),np.std(np.std(scores))))

结果

Original Class Dist: [212 357]

Fold:  1, Class dist: [153 256], Acc: 0.978
Fold:  2, Class dist: [153 256], Acc: 0.935
Fold:  3, Class dist: [153 256], Acc: 0.957
Fold:  4, Class dist: [153 256], Acc: 0.935
Fold:  5, Class dist: [153 256], Acc: 0.913
Fold:  6, Class dist: [153 257], Acc: 0.956
Fold:  7, Class dist: [153 257], Acc: 0.933
Fold:  8, Class dist: [153 257], Acc: 0.956
Fold:  9, Class dist: [153 257], Acc: 0.933
Fold: 10, Class dist: [153 257], Acc: 0.956
CV Accuracy: 0.945 +/- 0.018

CV Accuracy Scores: [0.97826087 0.93478261 0.95652174 0.93478261 0.91304348 0.95555556
 0.93333333 0.95555556 0.93333333 0.95555556]
CV Accuracy: 0.945 +/- 0.000

对比上述结果可知两点，其一，StratifiedKFold显然是根据照类别分布，按照比例采样形成训练集和测试集；其二，cross_val_score划分数据结果同StratifiedKFold采样结果是一致的。

参考文献：
Sebastian Raschka, Vahid Mirjalili. Python机器学习第二版. 南京：东南大学出版社，2018.

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