高级编程技术，第十五周

作业题目如下：

代码如下：

1.库的引用

from sklearn import datasets,cross_validation
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics

2.交叉检验划分

dataset = datasets.make_classification(n_samples=2000, n_features=15)
data, target = dataset[0], dataset[1]
kf = cross_validation.KFold(len(target), n_folds=10, shuffle=True)

3.机器学习部分代码

num = 1
for train_index, test_index in kf:
    data_train, target_train = data[train_index], target[train_index]
    data_test, target_test = data[test_index], target[test_index]
    print("Test:",num)
    num = num + 1
    
    print("GaussianNB：")
    clf = GaussianNB()
    clf.fit(data_train, target_train)
    pred = clf.predict(data_test)
    print("Accuracy：", metrics.accuracy_score(target_test, pred))
    print("F1-score:", metrics.f1_score(target_test, pred))
    print("AUC ROC:",metrics.roc_auc_score(target_test, pred))
    
    print("SVC：")
    clf = SVC(C=1e-01, kernel='rbf', gamma=0.1)
    clf.fit(data_train, target_train)
    pred = clf.predict(data_test)
    print("Accuracy：", metrics.accuracy_score(target_test, pred))
    print("F1-score:", metrics.f1_score(target_test, pred))
    print("AUC ROC:",metrics.roc_auc_score(target_test, pred))
    
    print("RandomForestClassifier：")
    clf = RandomForestClassifier(n_estimators=100)
    clf.fit(data_train, target_train)
    pred = clf.predict(data_test)
    print("Accuracy：", metrics.accuracy_score(target_test, pred))
    print("F1-score:", metrics.f1_score(target_test, pred))
    print("AUC ROC:",metrics.roc_auc_score(target_test, pred))
    print()

输出结果：

 

通过实验可知，二分类问题中，随机森林算法的效果比朴素贝叶斯和向量机都更好