BernoulliNB在sklearn软件包中用于预测的方法是什么?

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)

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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