第十五周作业

题目描述：

代码如下：

from sklearn import datasets  
from sklearn import cross_validation  
from sklearn.naive_bayes import GaussianNB  
from sklearn.svm import SVC  
from sklearn.ensemble import RandomForestClassifier  
from sklearn import metrics  
  
X,y = datasets.make_classification(n_samples = 1000, n_features = 10)  
print("X:",X)  
print("Y:",y)  
    
kf = cross_validation.KFold(1000, n_folds = 10, shuffle = True)  
for train_index, test_index in kf:  
    X_train, X_test = X[train_index], X[test_index]  
    y_train, y_test = y[train_index], y[test_index]  
  
print ("\nX_train:",X_train)  
print ("X_test:",X_test)  
print ("Y_train:",y_train)  
print ("Y_test:",y_test) 


#Gaussian 朴素贝叶斯方法：  
print("\nGaussian:")  
clf1 = GaussianNB()  
clf1.fit(X_train,y_train)  
pred1 = clf1.predict(X_test)  
print ("X的预测值：",pred1)  
print ("Y的测试值为：",y_test)

#SVC支持向量机方法  
print("\nSVC:")  
clf2 = SVC(C = 1e-01,kernel = 'rbf',gamma=0.1)  
clf2.fit(X_train,y_train)  
pred2 = clf2.predict(X_test)  
print ("X的预测值: ",pred2)  
print ("Y的测试值为：",y_test)  
  
#RandomForestClassifier  
print("\nRandomForestClassifier:")  
clf3 = RandomForestClassifier(n_estimators=6)  
clf3.fit(X_train, y_train)  
pred3 = clf3.predict(X_test)  
print ("X的预测值: ",pred3)  
print ("Y的测试值为：",y_test)  


def evaluate(y_test, pred, method):
	acc = metrics.accuracy_score(y_test, pred)
	f1 = metrics.f1_score(y_test, pred)
	auc = metrics.roc_auc_score(y_test, pred)
	print(method + ":")
	print ("Accuracy: ",acc) 
	print ("F1-score: ",f1) 
	print ("AUC ROC: ",auc) 
	print("\n")
	
print("\n性能评估")  
evaluate(y_test, pred1, "Guassian:")
evaluate(y_test, pred2, "Guassian:")
evaluate(y_test, pred3, "Guassian:")

运行结果：

Step 1：

Step 2：

Step 3：

Step 4：

Step 5：

所以从Step 4中可以看出，RandomForestClassifier方法更好