贝叶斯分类器——伯努利贝叶斯分类器

最新推荐文章于 2025-05-12 18:00:00 发布

原创最新推荐文章于 2025-05-12 18:00:00 发布 · 2.9k 阅读

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本文介绍并实现了伯努利贝叶斯分类器，通过Digits数据集进行训练和测试，展示了不同参数设置下分类器的表现变化，并探讨了参数α对分类效果的影响。

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模型原型
class sklearn.naive_bayes.BernoulliNB(alpha=1.0,binarize=0.0,fit_prior=True,class_prior=None)
参数
alpha
binarize:

None:假定原始数据已经二元化
浮点数:以该数值为界

fit_prior
class_prior:指定每个分类的先验概率
属性

class_logprior
feature_logprob
class_count_
featue_count_

方法

fit(X,y[,sample_weight])
partial_fix(X,y[,classes,sample_weight])
presict(X)
presict_log_proba(X)
predict_proba(X)
score(X,y[,sample_weight])

from sklearn import datasets,cross_validation,naive_bayes
import numpy as np
import matplotlib.pyplot as plt

显示Digit Dataset数据集

def show_digits():
    digits=datasets.load_digits()
    fig=plt.figure()
    print("vector from images 0:",digits.data[0])
    for i in range(25):
        ax=fig.add_subplot(5,5,i+1)
        ax.imshow(digits.images[i],cmap=plt.cm.gray_r,interpolation='nearest')
    plt.show()

show_digits()

加载数据

def load_data():
    digits=datasets.load_digits()
    return cross_validation.train_test_split(digits.data,digits.target,test_size=0.25,random_state=0)

测试伯努利贝叶斯分类器

def test_BernoulliNB(*data):
    X_train,X_test,y_train,y_test=data
    cls=naive_bayes.BernoulliNB()
    cls.fit(X_train,y_train)
    print('Training Score:%.2f'%cls.score(X_train,y_train))
    print('Testing Score:%.2f'%cls.score(X_test,y_test))

X_train,X_test,y_train,y_test=load_data()
test_BernoulliNB(X_train,X_test,y_train,y_test)

不同的a对伯努利贝叶斯分类器的影响

def test_BernoulliNB_alpha(*data):
    X_train,X_test,y_train,y_test=data
    alphas=np.logspace(-2,5,num=200)
    train_scores=[]
    test_scores=[]
    for alpha in alphas:
        cls=naive_bayes.BernoulliNB(alpha=alpha)
        cls.fit(X_train,y_train)
        train_scores.append(cls.score(X_train,y_train))
        test_scores.append(cls.score(X_test,y_test))
    #绘图
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(alphas,train_scores,label='Training Score')
    ax.plot(alphas,test_scores,label='Testing Score')
    ax.set_xlabel(r'$\alpha$')
    ax.set_ylabel('score')
    ax.set_ylim(0,1.0)
    ax.set_title('BernoulliNB')
    ax.set_xscale('log')
    ax.legend(loc='best')
    plt.show()

X_train,X_test,y_train,y_test=load_data()
test_BernoulliNB_alpha(X_train,X_test,y_train,y_test)