how can we get to know the selected and omitted features (columns ) names (header) using scikit-learn

Question

I am explaining the scenario with a piece of data:

Ex. data set.

GA_ID   PN_ID   PC_ID   MBP_ID  GR_ID   AP_ID   class
0.033   6.652   6.681   0.194         


        

Answer 1

Perhaps this code and commented explanations will help (adapted from here).

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)


forest.fit(X, y)

importances = forest.feature_importances_ #array with importances of each feature

idx = np.arange(0, X.shape[1]) #create an index array, with the number of features

features_to_keep = idx[importances > np.mean(importances)] #only keep features whose importance is greater than the mean importance
#should be about an array of size 3 (about)
print features_to_keep.shape

x_feature_selected = X[:,features_to_keep] #pull X values corresponding to the most important features

print x_feature_selected