参考了han同学的答案，数据集也可在han同学的github上下载。
3.4 选择两个 UCI 数据集，比较 10 折交叉验证法和留 法所估计出的对率回归的错误率.
import numpy as npfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import LeaveOneOutfrom sklearn.model_selection import KFoldfrom sklearn.model_selection import cross_val_scoredata_path = r'Transfusion.txt'data = np.loadtxt(data_path, delimiter=',').astype(int)X = data[:, :4]y = data[:, 4]# 样本数和属性数m, n = X.shape# 数据标准化X = (X - X.mean()) / X.std(0)# k-10kfold = KFold(n_splits=10)# print(kfold)lr = LogisticRegression(C=2)# score是准确率数组，cv代表多少折score = cross_val_score(lr, X, y, cv=kfold)print('acc of k-10: {}'.format(score.mean()))# LOO，留一法# LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples.loocv = LeaveOneOut()# loocv = KFold(m)score = cross_val_score(lr, X, y, cv=loocv)print('acc of loo: {}'.format(score.mean()))
《机器学习》课后习题 3.4 选择两个 UCI 数据集 比较 10 折交叉验证法和留 法所估计出的对率回归的错误率.
