邏輯回歸基礎知識可參考：http://blog.csdn.net/fjssharpsword/article/details/54580552

python內部算法已經實現了，最重要是理解y=f(x)的最小化損失函數并通過梯度下降法求解參數。

這里通過Python機器學習庫scikit-learn實現，代碼如下：

# -*- coding: utf-8 -*-import numpy as np import urllib from sklearn import preprocessing from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression from sklearn import metrics from sklearn.ensemble import ExtraTreesClassifier import timedef main(): #數據加載# load the CSV file as a numpy matrixdataset = np.loadtxt('D:\sample.csv', delimiter=",")# separate the data from the target attributesX = dataset[:,0:4]y = dataset[:,4]#數據標準化# normalize the data attributesnormalized_X = preprocessing.normalize(X)# standardize the data attributesstandardized_X = preprocessing.scale(X)#特征選取#model = LogisticRegression()#create the RFE model and select 3 attributes#rfe = RFE(model, 4)#rfe = rfe.fit(X, y)# summarize the selection of the attributes#print(rfe.support_)#print(rfe.ranking_)model = ExtraTreesClassifier()model.fit(X, y)# display the relative importance of each attributeprint(model.feature_importances_)#模型訓練model = LogisticRegression()model.fit(X, y)print(model)#模型預測# make predictionsexpected = ypredicted = model.predict(X)# summarize the fit of the modelprint(metrics.classification_report(expected, predicted))print(metrics.confusion_matrix(expected, predicted))#執行 if __name__ == '__main__': start = time.clock() main() end = time.clock() print('finish all in %s' % str(end - start)) sample.csv數據特點如下：最后一列是標簽

32,0,445,5,0 68,1,415,5,0 44,1,235,30,1 40,0,444,5,0 83,0,466,5,0 52,1,573,5,0 33,0,445,5,0

執行結果：

[ 0.15453444 0.00727297 0.63061708 0.2075755 ] LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,penalty='l2', random_state=None, solver='liblinear', tol=0.0001,verbose=0, warm_start=False)precision recall f1-score support0.0 0.66 0.87 0.75 2684981.0 0.66 0.35 0.45 188407avg / total 0.66 0.66 0.63 456905[[234680 33818][123182 65225]] finish all in 12.8994016037

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