朴素贝叶斯分类器（伯努利贝叶斯+高斯贝叶斯+多项式贝叶斯）

from sklearn.datasets import load_diabetes
X,y=load_diabetes().data,load_diabetes().target
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=8)
lr=LinearRegression().fit(X_train,y_train)
print("the coefficient:{}".format(lr.coef_))
print('the intercept:{}'.format(lr.intercept_))
print("the score of this model:{:.3f}".format(lr.score(X_test,y_test)))

import matplotlib.pyplot as plt
plt.scatter(X[:,0],X[:,1],c=y,cmap=plt.cm.spring,edgecolors='k')
plt.show()

#伯努利贝叶斯分类器
from sklearn.naive_bayes import BernoulliNB
bnb=BernoulliNB()
bnb.fit(X_train,y_train)
print("the score of this model:{}".format(bnb.score(X_test,y_test)))

import numpy as np
x_min=X[:,0].min()-0.5
x_max=X[:,0].max()+0.5
y_min=X[:,1].min()-0.5
y_max=X[:,1].max()+0.5
xx,yy=np.meshgrid(np.arange(x_min,x_max,.02),
                  np.arange(y_min,y_max,.02))
z=bnb.predict(np.c_[(xx.ravel(),yy.ravel())])
z=z.reshape(xx.shape)
plt.pcolormesh(xx,yy,z,cmap=plt.cm.Pastel1)
plt.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap=plt.cm.spring,edgecolors='k')
plt.scatter(X_test[:,0],X_test[:,1],c=y_test,cmap=plt.cm.spring,edgecolors='k',marker='*')
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.title("Classifier:BernoulliNB")
plt.show()

#高斯贝叶斯分类器
from sklearn.naive_bayes import GaussianNB
gnb=GaussianNB()
gnb.fit(X_train,y_train)
print("the score of this model:{}".format(gnb.score(X_test,y_test)))

import numpy as np
x_min=X[:,0].min()-0.5
x_max=X[:,0].max()+0.5
y_min=X[:,1].min()-0.5
y_max=X[:,1].max()+0.5
xx,yy=np.meshgrid(np.arange(x_min,x_max,.02),
                  np.arange(y_min,y_max,.02))
z=gnb.predict(np.c_[(xx.ravel(),yy.ravel())])
z=z.reshape(xx.shape)
plt.pcolormesh(xx,yy,z,cmap=plt.cm.Pastel1)
plt.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap=plt.cm.spring,edgecolors='k')
plt.scatter(X_test[:,0],X_test[:,1],c=y_test,cmap=plt.cm.spring,edgecolors='k',marker='*')
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.title("Classifier:GaussianNB")
plt.show()

#最大最小预处理，处理非负数据
from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler()
scaler.fit(X_train)
X_train_scaled=scaler.transform(X_train)
X_test_scaled=scaler.transform(X_test)
#多项式朴素贝叶斯
from sklearn.naive_bayes import MultinomialNB
mnb=MultinomialNB()
mnb.fit(X_train_scaled,y_train)
print("the score of this model:{}".format(mnb.score(X_test_scaled,y_test)))

import numpy as np
x_min=X[:,0].min()-0.5
x_max=X[:,0].max()+0.5
y_min=X[:,1].min()-0.5
y_max=X[:,1].max()+0.5
xx,yy=np.meshgrid(np.arange(x_min,x_max,.02),
                  np.arange(y_min,y_max,.02))
z=mnb.predict(np.c_[(xx.ravel(),yy.ravel())])
z=z.reshape(xx.shape)
plt.pcolormesh(xx,yy,z,cmap=plt.cm.Pastel1)
plt.scatter(X_train[:,0],X_train[:,1],c=y_train,cmap=plt.cm.spring,edgecolors='k')
plt.scatter(X_test[:,0],X_test[:,1],c=y_test,cmap=plt.cm.spring,edgecolors='k',marker='*')
plt.xlim(xx.min(),xx.max())
plt.ylim(yy.min(),yy.max())
plt.title("Classifier:MultinomialNB")
plt.show()

以上三个分类器均是二维可视化的。