sklearn中的朴素贝叶斯模型及其应用

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

　　150 6

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import MultinomialNB
gnb = MultinomialNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

　　150 7

 

from sklearn import datasets
iris = datasets.load_iris()

from sklearn.naive_bayes import BernoulliNB
gnb = BernoulliNB()
pred = gnb.fit(iris.data, iris.target)
y_pred = pred.predict(iris.data)

print(iris.data.shape[0],(iris.target != y_pred).sum())

　　150 100

 

from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score
gnb = GaussianNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

Accuracy:0.953

from sklearn.naive_bayes import BernoulliNB
from sklearn.model_selection import cross_val_score
gnb = BernoulliNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

　　Accuracy:0.333

 

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
gnb = MultinomialNB()
acores = cross_val_score(gnb, iris.data, iris.target, cv=10)
print("Accuracy:%.3f"%acores.mean())

　　Accuracy:0.953