[Python] How to get coefficients and feature importances from MultiOutputRegressor?

I am trying to perform a MultiOutput Regression using ElasticNet and Random Forests as follows:

from sklearn.ensemble import RandomForestRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.linear_model import ElasticNet

X_train, X_test, y_train, y_test = train_test_split(X_features, y, test_size=0.30,random_state=0)


Elastic Net

l1_range=np.arange(0.1,1.05,0.1).tolist()

regr_Enet=ElasticNetCV(cv=5,copy_X=True,n_alphas=100,l1_ratio=l1_range,selection='cyclic',normalize=False,verbose =2,n_jobs=1)

regr_multi_Enet= MultiOutputRegressor(regr_Enet)##ElasticNetCV

regr_multi_Enet.fit(X_train, y_train)


Random Forest

max_depth = 20
number_of_trees=100

regr_multi_RF=MultiOutputRegressor(RandomForestRegressor(n_estimators=number_of_trees,max_depth=max_depth,random_state=0,n_jobs=1,verbose=1))

regr_multi_RF.fit(X_train, y_train)

y_multirf = regr_multi_RF.predict(X_test)


Everything is going well, however I haven't found a way to obtain the coefficients (coef_ ) or most important features (feature_importances_) of the model. When I write:

regr_multi_Enet.coef_
regr_multi_RF.feature_importances_


It shows the following error:

AttributeError: 'MultiOutputRegressor' object has no attribute 'feature_importances_'
AttributeError: 'MultiOutputRegressor' object has no attribute 'coef_'


I have read the documentation on MultiOutputRegressor but I cannot find a way to extract the coefficients. How to retrieve them?

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