概述
Implementing Bag-of-Words Naive-Bayes classifier in NLTK
import numpy as np from nltk.probability import FreqDist from nltk.classify import SklearnClassifier from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_selection import SelectKBest,chi2 from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline pipeline = Pipeline([('tfidf',TfidfTransformer()),('chi2',SelectKBest(chi2,k=1000)),('nb',MultinomialNB())]) classif = SklearnClassifier(pipeline) from nltk.corpus import movie_reviews pos = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('pos')] neg = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('neg')] add_label = lambda lst,lab: [(x,lab) for x in lst] #Original code from thread: #classif.train(add_label(pos[:100],'pos') + add_label(neg[:100],'neg')) classif.train(add_label(pos,'pos') + add_label(neg,'neg'))#Made changes here #Original code from thread: #l_pos = np.array(classif.batch_classify(pos[100:])) #l_neg = np.array(classif.batch_classify(neg[100:])) l_pos = np.array(classif.batch_classify(pos))#Made changes here l_neg = np.array(classif.batch_classify(neg))#Made changes here print "Confusion matrix:\n%d\t%d\n%d\t%d" % ( (l_pos == 'pos').sum(),(l_pos == 'neg').sum(),(l_neg == 'pos').sum(),(l_neg == 'neg').sum())
运行此示例后,我收到了警告.
C:\Python27\lib\site-packages\scikit_learn-0.13.1-py2.7-win32.egg\sklearn\feature_selection\univariate_selection.py:327: UserWarning: Duplicate scores. Result may depend on feature ordering.There are probably duplicate features,or you used a classification score for a regression task. warn("Duplicate scores. Result may depend on feature ordering." Confusion matrix: 876 124 63 937
所以,我的问题是……
>有谁能告诉我这个错误信息是什么意思?
>我对原始代码进行了一些更改,但为什么混淆矩阵的结果比原始代码中的结果要高得多呢?
>如何测试此分类器的准确性?
混淆矩阵更高(或不同),因为您正在训练不同的数据.
混淆矩阵是衡量准确度的指标,并显示误报的数量等.阅读更多内容:http://en.wikipedia.org/wiki/Confusion_matrix
总结
以上是编程之家为你收集整理的用Python实例分类多项式朴素贝叶斯分类器全部内容,希望文章能够帮你解决用Python实例分类多项式朴素贝叶斯分类器所遇到的程序开发问题。
如果您也喜欢它,动动您的小指点个赞吧