如何为 scikit-learn 分类器获取信息量最大的特征? [英] How to get most informative features for scikit-learn classifiers?

问题描述

机器学习包中的分类器(如 liblinear 和 nltk)提供了一种方法 show_most_informative_features()，这对调试功能非常有帮助:

The classifiers in machine learning packages like liblinear and nltk offer a method show_most_informative_features(), which is really helpful for debugging features:

viagra = None          ok : spam     =      4.5 : 1.0
hello = True           ok : spam     =      4.5 : 1.0
hello = None           spam : ok     =      3.3 : 1.0
viagra = True          spam : ok     =      3.3 : 1.0
casino = True          spam : ok     =      2.0 : 1.0
casino = None          ok : spam     =      1.5 : 1.0

我的问题是是否为 scikit-learn 中的分类器实现了类似的功能.我搜索了文档，但找不到类似的内容.

My question is if something similar is implemented for the classifiers in scikit-learn. I searched the documentation, but couldn't find anything the like.

如果还没有这样的功能，有人知道如何获得这些值的解决方法吗?

If there is no such function yet, does somebody know a workaround how to get to those values?

推荐答案

在 larsmans 代码的帮助下，我想出了这个二进制案例的代码:

With the help of larsmans code I came up with this code for the binary case:

def show_most_informative_features(vectorizer, clf, n=20):
    feature_names = vectorizer.get_feature_names()
    coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
    top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])
    for (coef_1, fn_1), (coef_2, fn_2) in top:
        print "	%.4f	%-15s		%.4f	%-15s" % (coef_1, fn_1, coef_2, fn_2)

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