决策树 nltk

Question

我正在尝试不同的学习方法（决策树、NaiveBayes、MaxEnt）来比较它们的相对性能，以了解其中最好的方法。 如何实现决策树并获得其准确性？

import string
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
import nltk, nltk.classify.util, nltk.metrics
from nltk.classify import MaxentClassifier
from nltk.collocations import BigramCollocationFinder
from nltk.metrics import BigramAssocMeasures
from nltk.probability import FreqDist, ConditionalFreqDist
from sklearn import cross_validation
import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews

from nltk.classify import MaxentClassifier
from nltk.corpus import movie_reviews
from nltk.corpus import movie_reviews as mr

stop = stopwords.words('english')
words = [([w for w in mr.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]

def word_feats(words):
 return dict([(word, True) for word in words])

negids = movie_reviews.fileids('neg')
posids = movie_reviews.fileids('pos')

negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids]

negcutoff = len(negfeats)*3/4
poscutoff = len(posfeats)*3/4

trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
DecisionTree_classifier = DecisionTreeClassifier.train(trainfeats, binary=True, depth_cutoff=20, support_cutoff=20, entropy_cutoff=0.01)
print(accuracy(DecisionTree_classifier, testfeats))

Answer 1

您将不得不查看 nltk3 的代码（或文档字符串）。 nltk 书中给出的示例也有可能无需任何更改即可工作。见http://www.nltk.org/book/ch06.html#DecisionTrees

或者您可以只运行一个测试样本并自己计算误报率和误报率

这是你的准确性。