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交叉驗證的目的是為了有在訓(xùn)練集中有更多的數(shù)據(jù)點，以獲得最佳的學習效果，同時也希望有跟多的測試集數(shù)據(jù)來獲得最佳驗證。交叉驗證的要點是將訓(xùn)練數(shù)據(jù)平分到k個容器中，在k折交叉驗證中，將運行k次單獨的試驗，每一次試驗中，你將挑選k個訓(xùn)練集中的一個作為驗證集，剩下k-1個作為訓(xùn)練集，訓(xùn)練你的模型，用測試集測試你的模型。這樣運行k次，有十個不同的測試集，將十個測試集的表現(xiàn)平均，就是將這k次試驗結(jié)果取平均。這樣你就差不多用了全部數(shù)據(jù)去訓(xùn)練，也用全部數(shù)據(jù)去測試。

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#!/usr/bin/python"""Starter code for the validation mini-project.The first step toward building your POI identifier!Start by loading/formatting the dataAfter that, it's not our code anymore--it's yours! """import pickle import sys sys.path.append("../tools/") from feature_format import featureFormat, targetFeatureSplit from sklearn.metrics import accuracy_score from sklearn.cross_validation import train_test_split data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )### first element is our labels, any added elements are predictor ### features. Keep this the same for the mini-project, but you'll ### have a different feature list when you do the final project. features_list = ["poi", "salary"]data = featureFormat(data_dict, features_list) labels, features = targetFeatureSplit(data) features_train,features_test,labels_train,labels_test=train_test_split(features,labels,test_size=0.3,random_state=42) from sklearn.tree import DecisionTreeClassifier dlf=DecisionTreeClassifier() dlf.fit(features_train ,labels_train) f=dlf.predict(features_test) print accuracy_score(f,labels_test)### it's all yours from here forward!

　　

轉(zhuǎn)載于:https://www.cnblogs.com/fuhang/p/8512977.html

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