Python Machine Learning Blueprints - Second Edition

Random forest classifier method

Another method of modeling that tells us which features have an impact on our model is the feature importance that comes out of a random forest classifier. This more accurately reflects the true impact of a given feature.

Let's run our data through this type of model and examine the results:

from sklearn.ensemble import RandomForestClassifier 
clf_rf = RandomForestClassifier(n_estimators=1000) 
clf_rf.fit(X_train, y_train) 
 
f_importances = clf_rf.feature_importances_ 
 
f_names = X_train.columns 
f_std = np.std([tree.feature_importances_ for tree in clf_rf.estimators_], axis=0) 
 
zz = zip(f_importances, f_names, f_std) 
zzs = sorted(zz, key=lambda x: x[0], reverse=True) 
 
n_features = 10 imps = [x[0] for x in zzs[:n_features]] ...