tree.DecisionTree.feature_importances_ Numbers correspond to how features?
You can take the column names from X and tie it up with the feature_importances_ to understand them better. Here is an example - 

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import pandas as pd

clf = DecisionTreeClassifier(random_state=0)
iris = load_iris()
iris_pd = pd.DataFrame(iris.data, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
clf = clf.fit(iris_pd, iris.target)


I am taking the iris example, converting to a pandas.DataFrame() and fitting a simple DecisionTreeClassifier. Once the training is done, you can take the columns attribute of a pandas df and make a dict with the feature_importances_ output.

print(dict(zip(iris_pd.columns, clf.feature_importances_)))


This will give you what you want -

{'sepal_length': 0.0, 'sepal_width': 0.013333333333333329, 'petal_length': 0.064055958132045052, 'petal_width': 0.92261070853462157}


Hope this helps!