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