本文使用的資料型別是數值型，每一個樣本6個特徵表示，所用的資料如圖所示：

圖中A,B,C,D,E,F列表示六個特徵，G表示樣本標籤。每一行資料即為一個樣本的六個特徵和標籤。

實現LogisticRegression演算法的程式碼如下：

from sklearn.linear_model import LogisticRegression
import csv
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
data=[]
traffic_feature=[]
traffic_target=[]
csv_file = csv.reader(open('packSize_all.csv'))
for content in csv_file:
    content=list(map(float,content))
    if len(content)!=0:
        data.append(content)
        traffic_feature.append(content[0;6])//存放資料集特徵
        traffic_target.append(content[-1])//存放資料集標籤
print('data=',data)
print('traffic_feature=',traffic_feature)
print('traffic_target=',traffic_target)
feature_train, feature_test, target_train, target_test = train_test_split(traffic_feature, traffic_target, test_size=0.3,random_state=0)
LR = LogisticRegression()
LR.fit(feature_train,target_train)
predict_results=LR.predict(feature_test)
print(accuracy_score(predict_results, target_test))
conf_mat = confusion_matrix(target_test, predict_results)
print(conf_mat)
print(classification_report(target_test, predict_results))
 

執行結果如圖所示：