block12.python

# xgboost for feature importance on a classification problem
from xgboost import XGBClassifier
from matplotlib import pyplot
# define the model
model = XGBClassifier()
# fit the model
model.fit(X_clf, y_clf)
# get importance
importance = model.feature_importances_
# summarize feature importance
for i,v in enumerate(importance):
	print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.show()