朴素贝叶斯的K折交叉验证

Question

我正在尝试使用sklearn对我的朴素贝叶斯分类器进行k折验证

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() )

我从该网站https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience/history/969找到了代码。 在示例中，分类器是RandomForestClassifier，我想使用我自己的朴素贝叶斯分类器，但是我不太确定fit方法在这行上的作用probas = cfr.fit（train [traincv]，target [traincv]）。predict_proba （火车[testcv]）

Answer 1

似乎您只需要更改cfr，例如：

cfr = sklearn.naive_bayes.GaussianNB()

它应该工作相同。