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핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무

by 박해선, 오렐리앙 제롱
May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Hanbit Media, Inc.
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
110
1
머신러닝
cat
_
pipeline
=
Pipeline
([
('
selector
',
DataFrameSelector
(
cat
_
attribs
)),
('
cat
_
encoder
',
CategoricalEncoder
(
encoding
=
"
onehot
-
dense
")),
])
하지만 어떻게 이 두 파이프라인을 하나의 파이프라인으로 합칠 수 있을까요? 정답은 사이킷
런의
FeatureUnion
입니다. 변환기 목록 (또는 모두 변환기로 이뤄진 파이프라인 )을 전달하
transform
( )
메서드를 호출하면 각 변환기의
transform
( )
메서드를 병렬로 실행합니다.
그런 다음 각 변환기의 결과를 합쳐 반환합니다 (물론
fit
( )
메서드를 호출하면 각 변환기의
fit
( )
메서드를 실행합니다). 숫자형과 범주형 특성을 모두 다루는 전체 파이프라인은 다음
과 같습니다.
from sklearn
.
pipeline import FeatureUnion
full
_
pipeline
=
FeatureUnion
(
transformer
_
list
=
[
("
num
_
pipeline
",
num
_
pipeline
),
("
cat
_
pipeline
",
cat
_
pipeline
),
])
전체 파이프라인을 간단하게 실행할 수 있습니다.
>>>
housing
_
prepared
=
full
_
pipeline
.
fit
_
transform ...
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박해선, 오렐리앙 제롱

Publisher Resources

ISBN: 9791162240731