如何为不同类别的scikit learn分类器获得最丰富的信息？

Question

NLTK包提供了一个方法show_most_informative_features()来查找这两个类的最重要特性，输出如下：

   contains(outstanding) = True              pos : neg    =     11.1 : 1.0
        contains(seagal) = True              neg : pos    =      7.7 : 1.0
   contains(wonderfully) = True              pos : neg    =      6.8 : 1.0
         contains(damon) = True              pos : neg    =      5.9 : 1.0
        contains(wasted) = True              neg : pos    =      5.8 : 1.0

正如在这个问题How to get most informative features for scikit-learn classifiers?中所回答的，这也可以在scikit learn中工作。然而，对于二进制分类器，这个问题的答案只输出最好的特征本身。

所以我的问题是，我如何识别特征的相关类，就像上面的例子一样（杰出的在pos类中信息量最大，而seagal在否定类中信息量最大）？

编辑：事实上，我想要的是每节课最具信息性的单词列表。我该怎么做？谢谢！

Answer 1

左右两边的两个类可以得到相同的结果：

           precision    recall  f1-score   support

 Irrelevant       0.77      0.98      0.86       129
   Relevant       0.78      0.15      0.25        46

avg / total       0.77      0.77      0.70       175

    -1.3914 davis                   1.4809  austin
    -1.1023 suicide                 1.0695  march
    -1.0609 arrested                1.0379  call
    -1.0145 miller                  1.0152  tsa
    -0.8902 packers                 0.9848  passengers
    -0.8370 train                   0.9547  pensacola
    -0.7557 trevor                  0.7432  bag
    -0.7457 near                    0.7056  conditt
    -0.7359 military                0.7002  midamerica
    -0.7302 berlin                  0.6987  mark
    -0.6880 april                   0.6799  grenade
    -0.6581 plane                   0.6357  suspicious
    -0.6351 disposal                0.6348  death
    -0.5804 wwii                    0.6053  flight
    -0.5723 terminal                0.5745  marabi


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 ("\t%.4f\t%-15s\t\t%.4f\t%-15s" % (coef_1, fn_1, coef_2, fn_2))