（一）监督学习模型之线性分类器

一、解决问题

使用线性分类模型从事良／恶性肿瘤的预测任务

二、数据地址

http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data

三、代码实现

1、获得数据

import pandas as pdimport numpy as npcolumn_names = ['Sample code number', 'Clump Thickness', 'Uniformity of Call Size'               'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size',               'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'class']data = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', names = column_names)data.shape# drop nan# 去掉缺失值data = data.replace(to_replace='?', value=np.nan)data = data.dropna(how = 'any')data.shape

2、准备数据

from sklearn.cross_validation import train_test_splitX_train, X_test, y_train, y_test = train_test_split(data[column_names[1:9]], data[column_names[9]], test_size = 0.25, random_state = 33)y_train.value_counts()y_test.value_counts()

3、标准化数据

# 标准化数据，保证每个维度的特征数据方差为1，均值为0，# 让预测的结果不会被每个过大的特征值主导ss = StandardScaler()X_train = ss.fit_transform(X_train)X_test = ss.transform(X_test)

4、建立模型预测数据

lr = LogisticRegression()sgdc = SGDClassifier()lr.fit(X_train, y_train)lr_y_predict = lr.predict(X_test)sgdc.fit(X_train, y_train)sgdc_y_predict = sgdc.predict(X_test)

5、性能分析

from sklearn.metrics import classification_reportprint(lr.score(X_test, y_test))print(classification_report(y_test, lr_y_predict, target_names = ['Benign', 'Malignant']))print(classification_report(y_test, sgdc_y_predict, target_names = ['Benign', 'Malignant']))

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四、代码地址
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