Thinking-data-science-a-data-science-practitioners-guide

30 2 Dimensionality Reduction
This is the output in my run:
Support features
[ True True False True False False False False False True False]
As per this output, the first, second, fourth, and tenth features, which are marked
as True, are the most significant ones. You can also get the ranking of each feature by
examining the ranking_ attribute.
print('Features ranking by importance')
print(selector.ranking_)
This is the output in my run:
Features ranking by importance
[11413876512]
A ranking value of 1 shows higher significance. Thus, features at index 0, 1,
3, and 9 are most significant in our case. You can print the names of these four most
significant features by writing a small function:
def get_top_features():
rank_1=[]
for i in range(0,len(selector.ranking_)):
if selector.ranking_[i]==1:
rank_1.append(i)
print('The four most informative features are:')
print(X.iloc[:,rank_1].columns)
Calling this function gave the following output:
The four most informative features are:
Index(['Gender', 'Married', 'Education', 'Credit_History'],
dtype='object')
You observe that Gender, Married, Education, and Credit_History are the four
most significant features for our model training.