西瓜书 周志华 pdf,周志华西瓜书电子版

周志华西瓜书3.4题。
本文所编写的代码均使用python3.7进行调试，依靠的sklearn进行的实验。
第一步，导入iris数据集，数据集使用sklearn包里面自带的。

from sklearn.linear_model import LogisticRegressionfrom sklearn import model_selectionfrom sklearn.datasets import load_iris# 载入iris数据data = load_iris()

第二步，用10次十折交叉验证法估计对率回归的精度。（这里所用的循环即为10次）

# 十折交叉验证生成训练集和测试集def tenfolds(): k = 0 truth = [] while k < 10: kf = model_selection.KFold(n_splits=10, random_state=None, shuffle=True) for x_train_index, x_test_index in kf.split(data.data): x_train = data.data[x_train_index] y_train = data.target[x_train_index] x_test = data.data[x_test_index] y_test = data.target[x_test_index] # 验证生成数组长度是否符合规格 print(len(x_train),len(x_test)) # 用对率回归进行训练，拟合数据 log_model = LogisticRegression(multi_class= 'ovr', solver = 'liblinear') log_model.fit(x_train, y_train) # 用训练好的模型预测 y_pred = log_model.predict(x_test) for i in range(15): if y_pred[i] == y_test[i]: truth.append(y_pred[i] == y_test) k += 1 # 计算精度 accuracy = len(truth)/150 print("用10折交叉验证对率回归的精度是：", accuracy)

第三步，用留一法估计对率回归的精度。（这里循环了150次）

# 用留一法验证def leaveone(): loo = model_selection.LeaveOneOut() i = 0 true = 0 while i < 150: for x_train_index, x_test_index in loo.split(data.data): x_train = data.data[x_train_index] y_train = data.target[x_train_index] x_test = data.data[x_test_index] y_test = data.target[x_test_index] # 用对率回归进行训练，拟合数据 log_model = LogisticRegression(multi_class='ovr', solver='liblinear') log_model.fit(x_train, y_train) # 用训练好的模型预测 y_pred = log_model.predict(x_test) if y_pred == y_test: true += 1 i += 1 # 计算精度 accuracy = true / 150 print("用留一法验证对率回归的精度是：", accuracy)

注：使用的时候直接调用相应的定义函数即可。
主要参考的博文：https://blog.csdn.net/catherined/article/details/82015857
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