block5.python

# decision tree for feature importance on a regression problem
from sklearn.tree import DecisionTreeRegressor
from matplotlib import pyplot as plt
# define the model
model = DecisionTreeRegressor()
# fit the model
model.fit(X_reg, y_reg)
# 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
plt.bar([x for x in range(len(importance))], importance)
plt.show()