用Python实例分类多项式朴素贝叶斯分类器

Question

我正在寻找一个关于如何运行Multinomial Naive Bayes分类器的简单示例.我从StackOverflow中看到了这个例子:

在NLTK中实现Bag-of-Words朴素贝叶斯分类器

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......

1. 谁能告诉我这个错误信息是什么意思？
2. 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？
3. 如何测试此分类器的准确性？

Answer 1

原始代码对前 100 个正负样本进行训练，然后对其余样本进行分类。您已删除边界并在训练和分类阶段使用每个示例，换句话说，您具有重复的特征。要解决此问题，请将数据集分为两组：训练组和测试组。

混淆矩阵更高（或不同），因为您正在使用不同的数据进行训练。

混淆矩阵是准确性的衡量标准，显示误报的数量等。在此处阅读更多信息： http: //en.wikipedia.org/wiki/Confusion_matrix