Python – SkLearn Multinomial NB: Most Informative Features

classificationmachine learningpythonscikit-learntext-classification

As my classifier yields about 99% accuracy on test data, I am a bit suspicious and want to gain insight in the most informative features of my NB classifier to see what kind of features it is learning. The following topic has been very useful: How to get most informative features for scikit-learn classifiers?

As for my feature input, I am still playing around and at the moment I am testing a simple unigram model, using CountVectorizer:

 vectorizer = CountVectorizer(ngram_range=(1, 1), min_df=2, stop_words='english')

On the aforementioned topic I found the following function:

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)

Which gives the following result:

    -16.2420        114th                   -4.0020 said           
    -16.2420        115                     -4.6937 obama          
    -16.2420        136                     -4.8614 house          
    -16.2420        14th                    -5.0194 president      
    -16.2420        15th                    -5.1236 state          
    -16.2420        1600                    -5.1370 senate         
    -16.2420        16th                    -5.3868 new            
    -16.2420        1920                    -5.4004 republicans    
    -16.2420        1961                    -5.4262 republican     
    -16.2420        1981                    -5.5637 democrats      
    -16.2420        19th                    -5.6182 congress       
    -16.2420        1st                     -5.7314 committee      
    -16.2420        31st                    -5.7732 white          
    -16.2420        3rd                     -5.8227 security       
    -16.2420        4th                     -5.8256 states         
    -16.2420        5s                      -5.8530 year           
    -16.2420        61                      -5.9099 government     
    -16.2420        900                     -5.9464 time           
    -16.2420        911                     -5.9984 department     
    -16.2420        97                      -6.0273 gop

It works, but I would like to know what this function does in order to interpret the results. Mostly, I struggle with what the 'coef_' attribute does.

I understand that the left side is the top 20 feature names with lowest coefficients, and the right side the features with the highest coefficients. But how exactly does this work, how do I interpret this overview? Does it mean that the left side holds the most informative features for the negative class, and the right side the most informative features for the positive class?

Also, on the left side it kind of looks as if the feature names are sorted alphabetically, is this correct?

Best Answer

The coef_ attribute of MultinomialNB is a re-parameterization of the naive Bayes model as a linear classifier model. For a binary classification problems this is basically the log of the estimated probability of a feature given the positive class. It means that higher values mean more important features for the positive class.

The above print shows the top 20 lowest values (less predictive features) in the first column and the top 20 high values (highest predictive features) in the second column.

Related Solutions

Python – How to get most informative features for scikit-learn classifiers

The classifiers themselves do not record feature names, they just see numeric arrays. However, if you extracted your features using a Vectorizer/CountVectorizer/TfidfVectorizer/DictVectorizer, and you are using a linear model (e.g. LinearSVC or Naive Bayes) then you can apply the same trick that the document classification example uses. Example (untested, may contain a bug or two):

def print_top10(vectorizer, clf, class_labels):
    """Prints features with the highest coefficient values, per class"""
    feature_names = vectorizer.get_feature_names()
    for i, class_label in enumerate(class_labels):
        top10 = np.argsort(clf.coef_[i])[-10:]
        print("%s: %s" % (class_label,
              " ".join(feature_names[j] for j in top10)))

This is for multiclass classification; for the binary case, I think you should use clf.coef_[0] only. You may have to sort the class_labels.

Python – Recovering features names of explained_variance_ratio_ in PCA with sklearn

This information is included in the pca attribute: components_. As described in the documentation, pca.components_ outputs an array of [n_components, n_features], so to get how components are linearly related with the different features you have to:

Note: each coefficient represents the correlation between a particular pair of component and feature

import pandas as pd
import pylab as pl
from sklearn import datasets
from sklearn.decomposition import PCA

# load dataset
iris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)

# normalize data
from sklearn import preprocessing
data_scaled = pd.DataFrame(preprocessing.scale(df),columns = df.columns) 

# PCA
pca = PCA(n_components=2)
pca.fit_transform(data_scaled)

# Dump components relations with features:
print(pd.DataFrame(pca.components_,columns=data_scaled.columns,index = ['PC-1','PC-2']))

      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
PC-1           0.522372         -0.263355           0.581254          0.565611
PC-2          -0.372318         -0.925556          -0.021095         -0.065416

IMPORTANT: As a side comment, note the PCA sign does not affect its interpretation since the sign does not affect the variance contained in each component. Only the relative signs of features forming the PCA dimension are important. In fact, if you run the PCA code again, you might get the PCA dimensions with the signs inverted. For an intuition about this, think about a vector and its negative in 3-D space - both are essentially representing the same direction in space. Check this post for further reference.

Best Answer

Related Solutions

Python – How to get most informative features for scikit-learn classifiers

Python – Recovering features names of explained_variance_ratio_ in PCA with sklearn

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