I've been trying to get a grip on the importance of features used in a decision tree i've modelled. I'm interested in discovering the weight of each feature selected at the nodes as well as the term itself. My data is a bunch of documents. This is my code for the decision tree, I modified the code snippet from scikit-learn that extract ( http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html ):
from sklearn.feature_extraction.text import TfidfVectorizer
### Feature extraction
tfidf_vectorizer = TfidfVectorizer(stop_words=stopwords,
use_idf=True, tokenizer=None, ngram_range=(1,2))#ngram_range=(1,0)
tfidf_matrix = tfidf_vectorizer.fit_transform(data[:, 1])
terms = tfidf_vectorizer.get_features_names()
### Define Decision Tree and fit
dtclf = DecisionTreeClassifier(random_state=1234)
dt = data.copy()
y = dt["label"]
X = tfidf_matrix
fitdt = dtclf.fit(X, y)
from sklearn.datasets import load_iris
from sklearn import tree
### Visualize Devision Tree
with open('data.dot', 'w') as file:
tree.export_graphviz(dtclf, out_file = file, feature_names = terms)
file.close()
import subprocess
subprocess.call(['dot', '-Tpdf', 'data.dot', '-o' 'data.pdf'])
### Extract feature importance
importances = dtclf.feature_importances_
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print('Feature Ranking:')
for f in range(tfidf_matrix.shape[1]):
if importances[indices[f]] > 0:
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
print ("feature name: ", terms[indices[f]])
fitdt = dtclf.fit(X, y)
with open(...):
tree.export_graphviz(dtclf, out_file = file, feature_names = terms)
Thanks in advance
For you first question you need to get the feature names out of the vectoriser with terms = tfidf_vectorizer.get_feature_names()
. For your second question, you can you can call export_graphviz
with feature_names = terms
to get the actual names of your variables to appear in your visualisation (check out the full documentation of export_graphviz
for many other options that may be useful for improving your visualisation.
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