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DS-1000 / 900 / When using SelectKBest or SelectPercentile in sklearn.feature_selection, it's…

Problem

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Problem:

When using SelectKBest or SelectPercentile in sklearn.feature_selection, it's known that we can use following code to get selected features
np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
However, I'm not clear how to perform feature selection when using linear models like LinearSVC, since LinearSVC doesn't have a get_support method.
I can't find any other methods either. Am I missing something here? Thanks
Note use penalty='l1' and keep default arguments for others unless necessary

A:

<code>
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
corpus, y = load_data()
assert type(corpus) == list
assert type(y) == list
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
</code>
selected_feature_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
900
task_id
e2e9e0f3-1b52-5d41-9e80-29fc89c49920
task_key
default--test--900
task_revision_id
1
config / split
default / test

Official source

initial import