Using Python and scikitlearn, how to output the individual feature dependencies?

[warren8r]

Hello Jef,
Thanks again for your input. Ok, I have made some changes to my code:

from sklearn.ensemble import ExtraTreesClassifier
model = ExtraTreesClassifier()
model.fit(x_train, y_train)
coef = pd.DataFrame({''Columns'': x_train.columns, ''Importances'': np.transpose(model.feature_importances_)}).sort_values(by=[''Importances''], ascending=False)
print(coef.nlargest(10, ''Importances''))

I am getting the following output:

Output:
                                Columns  Importances
125      incident_severity_Minor Damage     0.042847
40                insured_hobbies_chess     0.041505
126        incident_severity_Total Loss     0.028544
124              collision_type_Unknown     0.019634
41            insured_hobbies_cross-fit     0.014173
1                       policy_state_OH     0.009765
16                     insured_sex_MALE     0.009697
57       insured_relationship_own-child     0.009582
25   insured_occupation_exec-managerial     0.009513
5                 policy_deductable_500     0.009146

I can't make sense of this, as the percentages don't seem right? Need they be calibrated or converted?
Thank you!