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핸즈온 비지도 학습
book

핸즈온 비지도 학습

by 강재원, 권재철, 안쿠르 A. 파텔
July 2020
Beginner to intermediate
444 pages
10h 50m
Korean
Hanbit Media, Inc.
Content preview from 핸즈온 비지도 학습
210
2부 사이킷런을 사용한 비지도 학습 모델
이러한 다른 포인트를 군집의
경계점
border
point
이라고 합니다.
일반적으로 최소 샘플수가 증가하면 군집 수가 감소합니다. 앞서 언급한
eps
와 마찬가지로
MNIST
데이터셋에 대한 최적의 최소 샘플수를 찾아야 합니다. 보다시피 군집에는 중심점과
경계점이 있지만 모든 면에서 그들은 동일한 그룹에 속합니다. 군집의 중심점 또는 경계점으로
그룹화되지 않는 모든 포인트는 이상치로 레이블됩니다.
5.5.2
MNIST
데이터셋에
DBSCAN
적용하기
이제 본격적인 실습으로 넘어가겠습니다. 이전과 마찬가지로
PCA
로 축소한
MNIST
데이터셋
의 처음
100
개 주성분에
DBSCAN
을 적용합니다.
from sklearn.cluster import DBSCAN
eps = 3
min_samples = 5
leaf_size = 30
n_jobs = 4
db = DBSCAN(eps=eps, min_samples=min_samples, leaf_size=leaf_size, n_jobs=n_jobs)
cutoff = 99
X_train_PCA_dbscanClustered = db.fit_predict(X_train_PCA.loc[:,0:cutoff])
X_train_PCA_dbscanClustered =
pd.DataFrame(data
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Publisher Resources

ISBN: 9791162243190