前言
此程序基于良/恶性肿瘤预测实验。
分别用LogisticRegression模型和SGDClassifier模型实现预测任务。
本程序可以流畅运行于Python3.6环境，但是Python2.x版本需要修正的地方也已经在注释中说明。
requirements:pandas,numpy,scikit-learn
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实验结果分析
LogisticRegression比起SGDClassifier在测试机上表现有更高的准确性，这是因为Scikit-learn中采用解析的方式精确计算LogisticRegression的参数，而使用梯度法估计SGDClassifier的参数。
相比之下，前者计算时间长但是模型性能略高；后者采用随机梯度上升算法估计模型参数，计算时间短，但是产出的模型性能略低。一般而言，对于训练数据规模在10万量级以上的数据，考虑到时间的耗用，更适合使用随机梯度算法对模型进行估计。
程序源码
import pandas as pd
import numpy as np
# features column names
column_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']
#read data from csv file
data = pd.read_csv('./breast-cancer-wisconsin.data',names=column_names)
#Data preprocessing
#replace all ? with standard missing value
data = data.replace(to_replace='?',value=np.nan)
#drop all data rows which have any missing feature
data=data.dropna(how='any')
# data.to_csv('./text.csv')# save data to csv file
#notes:you should use cross_valiation instead of model_valiation in python 2.7
#from sklearn.cross_validation import train_test_split #DeprecationWarning
from sklearn.model_selection import train_test_split #use train_test_split module of sklearn.model_valiation to split data
#take 25 percent of data randomly for testing,and others for training
X_train,X_test,y_train,y_test = train_test_split(data[column_names[1:10]],data[column_names[10]],test_size=0.25,random_state=33)
#check the numbers and category distribution of the test samples
# print(y_train.value_counts())
# print(y_test.value_counts())
#import relative package
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
#standardizing data in train set and test set
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
#initializing logisticregression and sgdcclassifier model
lr=LogisticRegression()
#notes:the default parameters in python2.7 are max_iter=5 tol=none,you can don't specify the parameters of sgdclassifier
#sgdc=SGDClassifier() #DeprecationWarning
sgdc=SGDClassifier(max_iter=5,tol=None)
#call fit function to trainning arguments ofmodel
lr.fit(X_train,y_train)
#save the prediction of test set in variable
lr_y_predict=lr.predict(X_test)
sgdc.fit(X_train,y_train)
sgdc_y_predict=sgdc.predict(X_test)
#performance analysis
from sklearn.metrics import classification_report
#get accuracy by the score function in LR model
print('Accuracy of LR Classifier:',lr.score(X_test,y_test))
#get precision ,recall and f1-score from classification_report module
print(classification_report(y_test,lr_y_predict,target_names=['Benign','Malignant']))
#get accuracy by the score function in SGD classifier
print('Accuracy of SGD Classifier:',sgdc.score(X_test,y_test))
#get precision ,recall and f1-score from classification_report module
print(classification_report(y_test,sgdc_y_predict,target_names=['Benign','Malignant']))
Ubuntu16.04 Python3.6 程序输出结果:
Accuracy of LR Classifier: 0.9883040935672515
precision recall f1-score support
Benign 0.99 0.99 0.99 100
Malignant 0.99 0.99 0.99 71
avg / total 0.99 0.99 0.99 171
Accuracy of SGD Classifier: 0.9824561403508771
precision recall f1-score support
Benign 1.00 0.97 0.98 100
Malignant 0.96 1.00 0.98 71
avg / total 0.98 0.98 0.98 171
数据下载地址
欢迎指正错误，包括英语和程序错误。有问题也欢迎提问，一起加油一起进步。
本程序完全是本人逐字符输入的劳动结果，转载请注明出处。