使用sklearn实现朴素贝叶斯分类器

from sklearn.naive_bayes import GaussianNB  #高斯朴素贝叶斯

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

datas = load_iris()
# print(datas)

iris_x = datas.data
iris_y = datas.target

# print(iris_x)
# print(iris_y)

iris_x0 = iris_x[ :, 0:2]
# print(iris_x0)

X_train,X_test,y_train,y_test =train_test_split(iris_x0, iris_y, test_size=0.3)

clf = GaussianNB( )

'''
GaussianNB 参数只有一个：先验概率priors

MultinomialNB参数有三个：alpha是常量，一般取值1，fit_prior是否考虑先验概率，class_prior自行输入先验概率

BernoulliNB参数有四个：前三个与MultinomialNB一样，第四个binarize 标签二值化

这里的参数的意义主要参考https://www.cnblogs.com/pinard/p/6074222.html'''

clf.fit(X_train,y_train)

per = clf.predict(X_test)
print(per)
print(y_test)