1.使用樸素貝葉斯模型對iris資料集進行花分類

嘗試使用3種不同型別的樸素貝葉斯：

高斯分佈型

多項式型

伯努利型

from sklearn import datasets
iris=datasets.load_iris()
from sklearn.naive_bayes import GaussianNB #高斯分佈型
gnb=GaussianNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=gnb.predict(iris.data)
print(iris.data.shape[0],(iris.target != y_pred).sum())

from sklearn import datasets
iris=datasets.load_iris()
from sklearn.naive_bayes import BernoulliNB #伯努利型
gnb=BernoulliNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=gnb.predict(iris.data)
print(iris.data.shape[0],(iris.target != y_pred).sum())

from sklearn import datasets
iris=datasets.load_iris()
 
from sklearn.naive_bayes import MultinomialNB #多項式型
gnb=MultinomialNB()
pred=gnb.fit(iris.data,iris.target)
y_pred=gnb.predict(iris.data)
print(iris.data.shape[0],(iris.target != y_pred).sum())

2..使用sklearn.model_selection.cross_val_score()，對模型進行驗證。

from sklearn.naive_bayes import GaussianNB #
 
高斯分佈型
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())

from sklearn.naive_bayes import BernoulliNB #伯努利型
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())

from sklearn.naive_bayes import MultinomialNB #多項式型
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())