K-折交叉验证的朴素贝叶斯

Question

我想为我的朴素贝叶斯分类器做一个k-折验证使用sklearn

train = csv_io.read_data("../Data/train.csv") 
target = np.array([x[0] for x in train]) 
train = np.array([x[1:] for x in train]) 

#In this case we'll use a random forest, but this could be any classifier 
cfr = RandomForestClassifier(n_estimators=100) 

#Simple K-Fold cross validation. 10 folds. 
cv = cross_validation.KFold(len(train), k=10, indices=False) 

#iterate through the training and test cross validation segments and 
#run the classifier on each one, aggregating the results into a list 
results = [] 
for traincv, testcv in cv: 
    probas = cfr.fit(train[traincv], target[traincv]).predict_proba(train[testcv]) 
    results.append(myEvaluationFunc(target[testcv], [x[1] for x in probas])) 

#print out the mean of the cross-validated results 
print "Results: " + str(np.array(results).mean()) 

我发现了一个代码从这个网站，https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience/history/969。在这个例子中，分类器是RandomForestClassifier，我想使用我自己的朴素贝叶斯分类器，但我不太确定这条线上的拟合方法做了什么probas = cfr.fit（train [traincv]，target [traincv]）。predict_proba （火车[testcv]）

Answer 1

好像你只需要改变CFR，例如：

cfr = sklearn.naive_bayes.GaussianNB() 

，它应该工作一样。