用sklearn进行线性分类

这里通过LogisticRegression分类器和随机梯度分类器对一些肿瘤数据进行分类，分出是良性还是恶性
代码来自《python机器学习及实践》

#pandas用于处理数据import pandas as pd                                                         import numpy as np#将数据分割为训练集和测试集from sklearn import  train_test_split#用于标准化数据                                            from sklearn.preprocessing import StandardScaler#一种线性分类技术                   from sklearn.linear_model import LogisticRegression#一种线性分类技术           from sklearn.linear_model import SGDClassifier  #用于分析数据                 from sklearn.metrics import classification_report                           #所有的属性和编号，标签colum_names = ['Sample code number',               'Clump Thickness',               'Uniformity of Cell Size',               'Uniformity of Cell Shape',               'Marginal Adhesion',               'Single Epithelial Cell Size',               'Bare Nuclei', 'Bland Chromatin',               'Normal Nucleoli',               'Mitoses',                'Class']#从csv文件中读入数据                                                            data = pd.read_csv('data.csv', names = colum_names)                             #将?用nan(非数字)替代data = data.replace(to_replace='?', value=np.nan)#放弃含有nan的数据                                 data = data.dropna(how='any')                                                       X_train, X_test, y_train, y_test = """属性为每行的1-9号值（左闭右开）label为每行的第11个属性测试集占25%随机数的种子为33"""train_test_split(data[colum_names[1:10]],                                       data[colum_names[10]],             test_size=0.25,                                         random_state=33)                ss = StandardScaler()X_train = ss.fit_transform(X_train)X_test = ss.fit_transform(X_test)lr = LogisticRegression()sgdc = SGDClassifier()lr.fit(X_train, y_train)lr_y_predict = lr.predict(X_test)                                                               #用fit之后的结果对X_test进行预测sgdc.fit(X_train, y_train)sgdc_y_predict = sgdc.predict(X_test)#对X_test的预测结果与y_test进行对比print('Accuracy of LR Classifier:', lr.score(X_test, y_test))#输出y_test与预测结果的对比                               print(classification_report(y_test, lr_y_predict, target_names=['Benign', 'Malignant']))        print('Accuarcy of SGD Classifier:', sgdc.score(X_test, y_test))print(classification_report(y_test, sgdc_y_predict, target_names=['Benign', 'Malignant']))