April 2019
Beginner to intermediate
480 pages
12h 34m
Korean
Content preview from 파이썬 라이브러리를 활용한 머신러닝(번역개정판): 사이킷런 핵심 개발자가 쓴 머신러닝과 데이터 과학 실무서
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5
장
모델 평가와 성능 향상
355
clf = Classier(**best_params)
clf.t(X[training_samples], y[training_samples])
# 테스트 세트를 사용해 평가합니다.
outer_scores.append(clf.score(X[test_samples], y[test_samples]))
return np.array(outer_scores)
이 함수를
iris
데이터셋에 적용해보겠습니다.
In [42]:
from sklearn.model_selection import ParameterGrid, StratiedKFold
scores = nested_cv(iris.data, iris.target, StratiedKFold(5),
StratiedKFold(5), SVC, ParameterGrid(param_grid))
print("교차 검증 점수:", scores)
Out [40]:
교차 검증 점수: [ 0.967 1. 0.967 0.967 1. ]
교차 검증과 그리드 서치 병렬화
그리드 서치는 데이터 용량이 크고 매개변수 수도 많을 때는 상당한 연산 부하를 일으키지만,
다행히 쉽게 병렬화할 수 있습니다.
19
하나의 교차 검증 분할에서 특정 매개변수 설정을 사용
해 모델을 만드는 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.