Tree Model for Feature Selection

tree_regression.py

from sklearn.tree import DecisionTreeRegressor

model = DecisionTreeRegressor()
# fit the model
model.fit(X_train, y_train)
# get importance
importance = model.feature_importances_
# summarize feature importance
impList = zip(X_train.columns, importance)
for feature in sorted(impList, key = lambda t: t[1], reverse=True):
    print(feature)
  
#Output - Important features
"""   ('enginesize', 0.6348884035234398)
      ('curbweight', 0.2389770360203148)
      ('horsepower', 0.03458620700119025)
      ('carwidth', 0.027170640676336785)
      ('stroke', 0.012516866412495744)
      ('peakrpm', 0.011750282673996262)
      ('carCompany_bmw', 0.009801675326218959)
      ('carlength', 0.008737911775028553) ..... """