BernoulliNB在sklearn软件包中用于预测的方法是什么? [英] what is the approach used by BernoulliNB in sklearn package for prediction?

问题描述

我正在阅读Sklearn中朴素贝叶斯的实现，但我无法理解BernoulliNB的预测部分:

I was reading up on the implementation of naive bayes in Sklearn, and I was not able to understand the predict part of BernoulliNB:

从

Code borrowed from source

def _joint_log_likelihood(self, X):
    #.. some code ommited

    neg_prob = np.log(1 - np.exp(self.feature_log_prob_))
    # Compute  neg_prob · (1 - X).T  as  ∑neg_prob - X · neg_prob
    jll = safe_sparse_dot(X, (self.feature_log_prob_ - neg_prob).T)
    jll += self.class_log_prior_ + neg_prob.sum(axis=1)

    return jll

neg_prob在其中的作用是什么.有人可以解释这种方法吗?

What is the role of neg_prob in this. Can someone explain this approach?

我在网上阅读的所有地方(来源)简单的方法是:

Everywhere I am reading online (source) the simple approach is that:

For word in document:
    For class in all_class:
        class_prob[class] += np.log(class_prob_for[word])
# basically add up the log probability of word given that class.
# (Which is pre computed from training data)

# finally add up the log probability of the class itself.

For class in all_class:
    class_prob[class] += np.log(class_prob_for[class])

但这与BernoulliNB

任何信息都将不胜感激.请让我知道是否需要添加更多详细信息，谢谢.

Any information is much appreciated. Please let me know if I should add more detail, thanks.

推荐答案

发现BernoulliNB与MultinomialNB略有不同.

如此处所述: http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/

文档中未出现的术语也用作:(1 - conditional_probability_of_term_in_class)

Terms which don't occur within the document are also used as: (1 - conditional_probability_of_term_in_class)

如Manning等人(2008)所述， 生成关于词汇相等的每个术语的布尔指示符 如果该术语属于审查文件，则为1；如果属于审查文件，则为0 不是.这种变化的模型与 多项式不仅因为没有考虑 每个单词出现的次数，也是因为它考虑了 在文档中说明非出现条款.而在 多项式模型中的非出现项被完全忽略，在 Bernoulli模型在计算条件时将其考虑在内 概率，因此没有条件.

The Bernoulli variation, as described by Manning et al (2008), generates a Boolean indicator about each term of the vocabulary equal to 1 if the term belongs to the examining document and 0 if it does not. The model of this variation is significantly different from Multinomial not only because it does not take into consideration the number of occurrences of each word, but also because it takes into account the non-occurring terms within the document. While in Multinomial model the non-occurring terms are completely ignored, in Bernoulli model they are factored when computing the conditional probabilities and thus the absence of terms is taken into account.

在sklearn中使用的算法来源: https ://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html

Algo used in sklearn Source: https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html

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