Post describes how to extract top feature names from a supervised learning classifier in sklearn.
Note: The training dataset
y_train are pandas dataframe with column names.
After fitting/training a classifier
clf, the scoring for features can be accessed (method varies depending on the classifier used).
- For example, for logistic regression it is the magnitude of the coefficients and can be accessed as
- For DecisionTree, it is
Sort the scores in descending order using
np.argsort() and pass it as an index to the column names of
# For Decision Tree classifier from sklearn.tree import DecisionTreeClassifier import numpy as np clf = DecisionTreeClassifier(random_state=42) clf.fit(X_train, y_train) importances = clf.feature_importances_ # printing top 5 features of fitted classifier print (X_train.columns[(np.argsort(importances)[::-1])][:5]) OR print(sorted(zip(X_train.columns,importances),key=lambda x: x)[::-1]