在朴素贝叶斯中获得功能重要性 [英] Getting feature importance in Naive bayes

问题描述

我的问题与此完全相似， 如何在朴素贝叶斯中获得特征重要性?

My problem is exactly similar to this, How to get feature Importance in naive bayes?

但是当我运行代码

neg_class_prob_sorted = NB_optimal.feature_log_prob_[0, :].argsort()
pos_class_prob_sorted = NB_optimal.feature_log_prob_[1, :].argsort()

print(np.take(count_vect.get_feature_names(), neg_class_prob_sorted[:10]))
print(np.take(count_vect.get_feature_names(), pos_class_prob_sorted[:10]))

它总是打印带有相同首字母的单词

it always prints words with same initials

['aafco' 'misfit' 'misfir' 'miseri' 'miser' 'misconcept' 'miscarriag'
 'misc' 'mirin' 'minuscul']
['aafco' 'metrx' 'meticul' 'methood' 'metaplus' 'metaphor' 'mestemach'
 'messiest' 'mesmer' 'mesa']

有什么更好的方法可以打印出与正反两类相对应的重要特征?

Is there any better way I can print the important features corrosponding to positive and negative class?

推荐答案

important_features = pd.DataFrame(data = np.transpose(model.fit(fdata，y_train).feature_log_prob _).astype("float32")，index = fdata.columns)

important_features= pd.DataFrame(data=np.transpose(model.fit(fdata, y_train).feature_log_prob_).astype("float32"),index=fdata.columns)

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