I'm calling xgboost via its scikit-learn-style Python interface:
model = xgboost.XGBRegressor()
%time model.fit(trainX, trainY)
testY = model.predict(testX)
Some sklearn models tell you which importance they assign to features via the attribute feature_importances. This doesn't seem to exist for the XGBRegressor:
model.feature_importances_
AttributeError Traceback (most recent call last)
<ipython-input-36-fbaa36f9f167> in <module>()
----> 1 model.feature_importances_
AttributeError: 'XGBRegressor' object has no attribute 'feature_importances_'
The weird thing is: For a collaborator of mine the attribute feature_importances_ is there! What could be the issue?
These are the versions I have:
In [2]: xgboost.__version__
Out[2]: '0.6'
In [4]: sklearn.__version__
Out[4]: '0.18.1'
... and the xgboost C++ library from github, commit ef8d92fc52c674c44b824949388e72175f72e4d1.
How did you install xgboost? Did you build the package after cloning it from github, as described in the doc?
http://xgboost.readthedocs.io/en/latest/build.html
As in this answer:
Feature Importance with XGBClassifier
There always seems to be a problem with the pip-installation and xgboost. Building and installing it from your build seems to help.
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