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）

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

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

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

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

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

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

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

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

以上三个分类器均是二维可视化的。