基于sklearn实现Bagging算法（python）

 from sklearn.ensemble import BaggingClassifier from sklearn.tree import D<div style="color:transparent">来源gaodai^.ma#com搞#代!码网</div>ecisionTreeClassifier from sklearn.preprocessing import StandardScaler 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])//存放数据集的特征 traffic_target.append(content[-1])//存放数据集的标签 print('data=',data) print('traffic_feature=',traffic_feature) print('traffic_target=',traffic_target) scaler = StandardScaler() # 标准化转换 scaler.fit(traffic_feature) # 训练标准化对象 traffic_feature= scaler.transform(traffic_feature) # 转换数据集 feature_train, feature_test, target_train, target_test = train_test_split(traffic_feature, traffic_target, test_size=0.3,random_state=0) tree=DecisionTreeClassifier(criterion='entropy', max_depth=None) # n_estimators=500:生成500个决策树 clf = BaggingClassifier(base_estimator=tree, n_estimators=500, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, n_jobs=1, random_state=1) clf.fit(feature_train,target_train) predict_results=clf.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))