How to find the features names of the coefficients using scikit linear regression?

Question

#training the model model_1_features = ['sqft_living', 'bathrooms', 'bedrooms', 'lat', 'long'] model_2_features = model_1_features + ['bed_bath_rooms'] model_3_features = model_2_features + ['bedrooms_squared', 'log_sqft_living', 'lat_plus_long']  model_1 = linear_model.LinearRegression() model_1.fit(train_data[model_1_features], train_data['price'])  model_2 = linear_model.LinearRegression() model_2.fit(train_data[model_2_features], train_data['price'])  model_3 = linear_model.LinearRegression() model_3.fit(train_data[model_3_features], train_data['price'])  # extracting the coef print model_1.coef_ print model_2.coef_ print model_3.coef_ 

If I change the order of the features, the coef are still printed in the same order, hence I would like to know the mapping of the feature with the coeff

Answer 1

The trick is that right after you have trained your model, you know the order of the coefficients:

model_1 = linear_model.LinearRegression() model_1.fit(train_data[model_1_features], train_data['price']) print(list(zip(model_1.coef_, model_1_features))) 

This will print the coefficients and the correct feature. (Tested with pandas DataFrame)

If you want to reuse the coefficients later you can also put them in a dictionary:

coef_dict = {} for coef, feat in zip(model_1.coef_,model_1_features):     coef_dict[feat] = coef 

(You can test it for yourself by training two models with the same features but, as you said, shuffled order of features.)

Answer 2

@Robin posted a great answer, but for me I had to make one tweak on it to work the way I wanted, and it was to refer to the dimension of the 'coef_' np.array that I wanted, namely modifying to this: model_1.coef_[0,:], as below:

coef_dict = {} for coef, feat in zip(model_1.coef_[0,:],model_1_features):     coef_dict[feat] = coef 

Then the dict was created as I pictured it, with {'feature_name' : coefficient_value} pairs.