yzhao062/pyod표 형식, 시계열, 그래프, 텍스트, 이미지, 오디오 데이터에 걸친 이상 탐지를 위한 Python 라이브러리입니다. 60개 이상의 탐지기, 벤치마크 기반의 ADEngine 오케스트레이션, 그리고 AI 에이전트를 위한 에이전틱 워크플로우를 제공합니다.
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PyOD 3: 대규모 에이전트 기반 이상 탐지
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**PyOD는 에이전트를 지원합니다.** Claude Code와 Codex는 ``od-expert`` 스킬을 사용하여 ADEngine 조사를 수행할 수 있으며, MCP 호환 에이전트는 PyOD의 탐지기 지식 및 계획 도구를 쿼리할 수 있습니다. 기존의 ``fit``/``predict`` API는 그대로 유지됩니다.
PyOD 3는 이상 탐지를 위한 가장 포괄적인 Python 라이브러리입니다. 네 가지 핵심 축:
=========================== ========================================================================================
영역 의미
=========================== ========================================================================================
멀티모달 표형, 시계열, 그래프, 텍스트, 이미지, 오디오 데이터를 하나의 API로 처리하는 61개 탐지기
전체 수명 주기 원시 데이터부터 설명된 이상치와 다음 단계 안내까지 단일 호출로 처리
에이전트 기반 od-expert는 자연어 요청을 ADEngine 워크플로로 변환하고, MCP는 다른 에이전트를 위한 구조화된 도구를 제공
최다 사용 4,600만 회 이상 다운로드; 벤치마크 기반 라우팅 (ADBench, TSB-AD, BOND, NLP-ADBench)
=========================== ========================================================================================
설치 ^^^^^^^
핵심 라이브러리 (모든 활성화 경로에 필요):
.. code-block:: bash
pip install pyod
그런 다음 에이전트 스택에 맞는 활성화 경로를 선택하세요:
.. code-block:: bash
# 1. Claude Code / Codex — enables the od-expert skill
pyod install skill # Claude Code: user-global (~/.claude/skills/)
pyod install skill --project # Codex: project-local (./skills/, Codex has no user-global dir)
# 2. Any MCP-compatible LLM — requires the optional mcp extra
pip install pyod[mcp]
pyod mcp serve # alias for `python -m pyod.mcp_server`
# 3. Pure Python — no extra step
# from pyod.utils.ad_engine import ADEngine
pyod info를 실행하면 버전, 탐지기 수, 각 활성화 경로의 설치 상태를 확인할 수 있습니다. pyod info는 설치된 에이전트 스택(Claude Code의 ~/.claude/, Codex의 ~/.codex/)도 감지하여 올바른 설치 명령을 추천합니다.
conda, 소스 설치, 의존성 세부 정보 및 문제 해결은 전체 설치 가이드 <https://pyod.readthedocs.io/en/latest/install.html>__를 참조하세요. v3.0.0의 레거시 pyod-install-skill 명령은 pyod install skill의 별칭으로 계속 작동합니다.
5줄 코드로 하는 이상치 탐지 (pip install pyod):
.. code-block:: python
from pyod.models.iforest import IForest
clf = IForest()
clf.fit(X_train)
y_train_scores = clf.decision_scores_ # training anomaly scores
y_test_scores = clf.decision_function(X_test) # test anomaly scores
PyOD를 사용하는 세 가지 방법:
========= ===================== ====================================================================== =======================================
계층 이름 사용 시점 진입점
========= ===================== ====================================================================== =======================================
1 클래식 API 사용하려는 탐지기를 아는 경우 계층 1 예제 <https://pyod.readthedocs.io/en/latest/examples/tabular.html>__
2 ADEngine PyOD가 자동으로 선택, 비교, 평가해 주기를 원하는 경우 계층 2 튜토리얼 <https://pyod.readthedocs.io/en/latest/examples/adengine.html>__
3 에이전트 기반 조사 AI 에이전트가 자연어 대화를 통해 OD를 수행하길 원하는 경우 계층 3 튜토리얼 <https://pyod.readthedocs.io/en/latest/examples/agentic.html>__
========= ===================== ====================================================================== =======================================
계층 2와 3은 PyOD의 수명 주기 오케스트레이션 핵심인 ADEngine으로 구동됩니다. 계층 3의 전체 다중 턴 조사 흐름은 Claude Code 및 Codex용 od-expert 스킬을 통해 사용할 수 있습니다. MCP 서버(python -m pyod.mcp_server)는 MCP 호환 LLM을 위한 10개의 상태 비저장 도구를 제공하며, 지식 조회(list_detectors, explain_detector, compare_detectors, get_benchmarks), 계획(profile_data, plan_detection, build_detector), 탐지(run_detection, analyze_results, explain_findings)를 아우릅니다. 상태 저장형 investigate / MCP 도구는 나중에 제공될 예정입니다.
.. image:: https://raw.githubusercontent.com/yzhao062/pyod/development/docs/figs/agentic-demo.png :alt: PyOD 3 agentic investigation demo on cardiotocography dataset :align: center :width: 720
위 그림은 UCI Cardiotocography 데이터셋에서 수행된 실제 5턴 에이전트 대화를 보여줍니다. 전체 워크스루 <https://pyod.readthedocs.io/en/latest/examples/agentic.html>, 실행 가능한 에이전트 예제 <https://github.com/yzhao062/pyod/blob/development/examples/agentic_example.py>, 또는 대화형 HTML 데모 <https://htmlpreview.github.io/?https://github.com/yzhao062/pyod/blob/development/examples/agentic_demo.html>__를 참조하세요.
PyOD 생태계 및 리소스:
NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>__ (NLP 이상 탐지) | TODS <https://github.com/datamllab/tods>__ (시계열) | PyGOD <https://pygod.org/>__ (그래프) | ADBench <https://github.com/Minqi824/ADBench>__ (벤치마크) | AD-LLM <https://arxiv.org/abs/2412.11142>__ (LLM 기반 이상 탐지) [#Yang2024ad]_ | 리소스 <https://github.com/yzhao062/anomaly-detection-resources>__
PyOD 소개 ^^^^^^^^^^
2017년에 설립된 PyOD는 가장 오래 운영되었고 가장 널리 사용되는 이상 탐지용 Python 라이브러리입니다. 4,600만 회 이상의 다운로드 <https://pepy.tech/project/pyod>를 기록하며 학술 연구(Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>, KDnuggets <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>__, Towards Data Science <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__에 소개됨)와 상용 제품 모두에서 사용됩니다.
V3는 고전적인 fit/predict API를 완전히 하위 호환되도록 유지하면서 ADEngine(수명 주기 오케스트레이션)과 od-expert 스킬(에이전트 워크플로)을 추가합니다. V3는 빠른 병렬 학습을 위해 SUOD [#Zhao2021SUOD]_를 기반으로 하고, 모델별 속도 향상을 위해 numba JIT를 사용합니다.
영향 및 인정:
=================================== ===========================================================================
영역 사례
=================================== ===========================================================================
우주 및 과학 유럽 우주국(ESA) OPS-SAT 우주선 원격 측정 벤치마크 <https://www.nature.com/articles/s41597-025-05035-3>__ (Nature Scientific Data, 2025)는 30가지 전체 알고리즘에 PyOD를 사용합니다.
기업 배포 Walmart(일일 가격 업데이트 100만 건 이상, KDD 2019), Databricks(PyOD를 MLflow/Hyperopt와 통합한 Kakapo 프레임워크, 내부자 위협 탐지 솔루션), IQVIA(약국 청구 12만 3천 건 이상), Altair AI Studio, Ericsson(특허 WO2023166515A1 <https://patents.google.com/patent/WO2023166515A1>).
도서 Outlier Detection in Python <https://www.manning.com/books/outlier-detection-in-python> (Brett Kennedy, Manning); Handbook of Anomaly Detection with Python (Chris Kuo, Columbia); Finding Ghosts in Your Data <https://link.springer.com/book/10.1007/978-1-4842-8870-2>__ (Kevin Feasel, Apress).
강좌 DataCamp Anomaly Detection in Python <https://www.datacamp.com/courses/anomaly-detection-in-python>__ (플랫폼 학습자 1,900만 명 이상), Manning liveProject <https://www.manning.com/liveproject/using-pyod-and-ensembles-methods>, O'Reilly 비디오 에디션, 다수의 Udemy 강좌.
팟캐스트 Talk Python To Me #497 <https://talkpython.fm/episodes/show/497/outlier-detection-with-python>, 전체 문서 번역), 일본어, 한국어, 독일어, 스페인어.
=================================== ===========================================================================
인용, 기업 배포, 특허, 언론 보도의 전체 목록은 Read the Docs의 전체 영향 페이지 <https://pyod.readthedocs.io/en/latest/impact.html>__를 참조하세요.
PyOD 인용:
과학 출판물에서 PyOD를 사용하신다면 다음 논문을 인용해 주시면 감사하겠습니다:
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection <https://arxiv.org/abs/2412.12154>__은 프리프린트로 제공됩니다. 과학 출판물에서 PyOD를 사용하신다면 다음 논문을 인용해 주시면 감사하겠습니다::
@inproceedings{chen2025pyod,
title={Pyod 2: A python library for outlier detection with llm-powered model selection},
author={Chen, Sihan and Qian, Zhuangzhuang and Siu, Wingchun and Hu, Xingcan and Li, Jiaqi and Li, Shawn and Qin, Yuehan and Yang, Tiankai and Xiao, Zhuo and Ye, Wanghao and others},
booktitle={Companion Proceedings of the ACM on Web Conference 2025},
pages={2807--2810},
year={2025}
}
PyOD 논문 <http://www.jmlr.org/papers/volume20/19-011/19-011.pdf>은 Journal of Machine Learning Research (JMLR) <http://www.jmlr.org/> (MLOSS 트랙)에 게재되었습니다.::
@article{zhao2019pyod,
author = {Zhao, Yue and Nasrullah, Zain and Li, Zheng},
title = {PyOD: A Python Toolbox for Scalable Outlier Detection},
journal = {Journal of Machine Learning Research},
year = {2019},
volume = {20},
number = {96},
pages = {1-7},
url = {http://jmlr.org/papers/v20/19-011.html}
}
또는::
Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.
이상 탐지에 대한 더 넓은 관점을 얻으려면 ADBench <https://arxiv.org/abs/2206.09426>__ [#Han2022ADBench]_ 및 ADGym <https://arxiv.org/abs/2309.15376>__에 관한 NeurIPS 논문을 참조하세요.
목차:
API 치트시트 및 참조 <#api-cheatsheet--reference>__벤치마크 <#benchmarks>__구현된 알고리즘 <#implemented-algorithms>__ (표형, 시계열, 그래프, 임베딩)추가 주제 <#additional-topics>__ (모델 저장/불러오기, SUOD, 임계값 설정)이상치 탐지 빠른 시작 <#quick-start-for-outlier-detection>__기여 방법 <#how-to-contribute>__포함 기준 <#inclusion-criteria>__API 치트시트 및 참조 ^^^^^^^^^^^^^^^^^^^^^^^^^^
전체 API 참조는 PyOD Documentation <https://pyod.readthedocs.io/en/latest/>에서 모달리티별로 나뉩니다: Tabular <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>, Time Series <https://pyod.readthedocs.io/en/latest/pyod.models.timeseries.html>, Graph <https://pyod.readthedocs.io/en/latest/pyod.models.graph.html>, Embedding <https://pyod.readthedocs.io/en/latest/pyod.models.embedding.html>, ADEngine <https://pyod.readthedocs.io/en/latest/pyod.ad_engine.html>, Utilities <https://pyod.readthedocs.io/en/latest/pyod.utils.html>__. 아래는 모든 탐지기에 대한 빠른 치트시트입니다:
학습된 모델의 주요 속성:
벤치마크 ^^^^^^^^^^
ADBench <https://github.com/Minqi824/ADBench>__ [#Han2022ADBench]_: 57개 표형 데이터셋에 대한 30가지 알고리즘. 비교 <https://github.com/yzhao062/pyod/blob/master/examples/compare_all_models.py>__ 참조.NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>__: 8개 텍스트 데이터셋에 대한 19가지 방법. 2단계(임베딩 + 탐지기) 방식이 end-to-end 방식보다 우수합니다.TSB-AD <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_: 1070개 시계열 데이터셋에 대한 40가지 알고리즘 (NeurIPS 2024).BOND <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_: 14개 데이터셋에 대한 14가지 그래프 이상 탐지 알고리즘 (NeurIPS 2022).추가 주제 ^^^^^^^^^^^^^^^^^
모델 저장 및 불러오기 <https://pyod.readthedocs.io/en/latest/model_persistence.html>: joblib 또는 pickle을 사용하여 PyOD 모델을 저장하고 불러올 수 있습니다. 예제 <https://github.com/yzhao062/pyod/blob/master/examples/save_load_model_example.py> 참조.SUOD로 빠른 학습 <https://pyod.readthedocs.io/en/latest/fast_train.html>: SUOD 프레임워크로 학습 및 예측을 가속화합니다 [#Zhao2021SUOD]_. 예제 <https://github.com/yzhao062/pyod/blob/master/examples/suod_example.py> 참조.이상 점수 임계값 설정 <https://pyod.readthedocs.io/en/latest/thresholding.html>__: PyThresh <https://github.com/KulikDM/pythresh>__를 통해 오염 수준(contamination level)을 설정하는 데이터 기반 접근법.구현된 알고리즘 ^^^^^^^^^^^^^^^^^^^^^^
PyOD는 두 가지 기능 그룹으로 구성됩니다: (i) 탐지 알고리즘(Detection Algorithms) — 표형, 시계열, 그래프, 오디오 데이터를 위한 전용 하위 섹션이 있으며(표형 테이블의 EmbeddingOD는 파운데이션 모델 인코더를 통해 텍스트 및 이미지 지원을 추가합니다); (ii) 유틸리티 함수(Utility Functions) — 데이터 생성, 평가, 수명 주기 오케스트레이션을 위한 함수입니다.
(i-a) 표형 및 멀티모달 탐지 알고리즘:
.. list-table:: :widths: 15 14 58 5 8 :header-rows: 1* - 유형 - 약어 - 알고리즘 - 연도 - 참조
예제 <https://github.com/yzhao062/pyod/blob/development/examples/ecod_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/abod_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/copod_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/mad_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/sos_example.py>__)앙상블 방법(IForest, INNE, DIF, FB, LSCP, LODA, SUOD, XGBOD)은 위 표에 포함되어 있습니다. 점수 결합 함수(average, maximization, AOM, MOA, median, majority vote)는 pyod.models.combination에 있습니다. 자세한 내용은 API 문서 <https://pyod.readthedocs.io/en/latest/pyod.models.tabular.html>__를 참조하세요.
(i-b) 시계열 이상 탐지 :
모든 시계열 탐지기는 표 형식(tabular) 탐지기와 동일한 fit/predict/decision_function API를 사용합니다. 단, 한 가지 예외가 있습니다. MatrixProfile은 트랜스덕티브 방식입니다 (훈련 전용; fit() 후 decision_scores_와 labels_를 사용하며, 표본 외 predict는 없습니다).
입력 형식: 단변량의 경우 shape (n_timestamps,), 다변량의 경우 (n_timestamps, n_channels)인 numpy 배열입니다. 각 행은 하나의 타임스텝이며, 열은 채널/피처입니다. Pandas DataFrame과 리스트는 자동 변환됩니다. 출력: 타임스텝마다 하나의 이상 점수를 가진 shape (n_timestamps,)의 decision_scores_입니다.
3줄로 시계열 탐지하기:
.. code-block:: python
from pyod.models.ts_kshape import KShape # or any TS detector
clf = KShape(window_size=20)
clf.fit(X_train) # shape (n_timestamps,) or (n_timestamps, n_channels)
scores = clf.decision_scores_ # per-timestamp anomaly scores
TSB-AD 벤치마크 <https://github.com/TheDatumOrg/TSB-AD>__ [#Liu2024TSB]_ (NeurIPS 2024, 1070개 데이터셋) 기준 알고리즘 순위:
.. list-table:: :widths: 15 18 50 5 12 :header-rows: 1
예제 <https://github.com/yzhao062/pyod/blob/development/examples/ts_od_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/ts_matrix_profile_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/ts_spectral_residual_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/ts_kshape_example.py>__)예제 <https://github.com/yzhao062/pyod/blob/development/examples/ts_sand_example.py>__)(i-c) 그래프 이상 탐지 (pip install pyod[graph]):
v1에서 모든 그래프 탐지기는 트랜스덕티브 방식입니다. fit() 후 decision_scores_와 labels_를 사용합니다. 표본 외 predict는 없습니다. 입력: x(노드 피처)와 edge_index(COO 간선)를 포함하는 PyG Data 객체입니다. SCAN은 피처 없이도 작동합니다.
3줄로 그래프 탐지하기 (pip install pyod[graph]):
.. code-block:: python
from pyod.models.pyg_dominant import DOMINANT
clf = DOMINANT(hidden_dim=64, epochs=100)
clf.fit(data) # PyG Data object
scores = clf.decision_scores_ # per-node anomaly scores
BOND 벤치마크 <https://arxiv.org/abs/2206.10071>__ [#Liu2022BOND]_ (NeurIPS 2022, 14개 데이터셋) 기준 알고리즘 순위:
.. list-table:: :widths: 18 18 45 5 14 :header-rows: 1
dominant 예제 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_dominant_example.py>__)cola 예제 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_cola_example.py>__)conad 예제 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_conad_example.py>__)anomalydae 예제 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_anomalydae_example.py>__)guide 예제 <https://github.com/yzhao062/pyod/blob/development/examples/pyg_guide_example.py>__)(i-d) 오디오 이상 탐지 (pip install pyod[audio]):
오디오 클립은 동일한 fit/decision_function API를 사용합니다. 두 가지 경로를 사용할 수 있습니다. 가벼운 임베딩 후 탐지(embed-then-detect) 경로(EmbeddingOD.for_audio()는 각 클립을 74차원 수작업 설계 음향 벡터로 변환하고 일반적인 탐지기를 실행)와 전용 딥 탐지기(AudioAE, 로그-멜 재구성 오토인코더)가 있습니다. 입력은 파일 경로, 파형 배열 또는 (waveform, sample_rate) 튜플입니다. 출력: 클립당 하나의 이상 점수입니다.
3줄로 오디오 탐지하기 (pip install pyod[audio]):
.. code-block:: python
from pyod.models.embedding import EmbeddingOD
clf = EmbeddingOD.for_audio('balanced') # 74-dim handcrafted features + KNN
clf.fit(train_clips) # list of file paths or waveform arrays
scores = clf.decision_scores_ # per-clip anomaly scores
.. list-table:: :widths: 18 18 45 5 14 :header-rows: 1
for_audio(): 74차원 MFCC, chroma 및 spectral 피처를 모든 탐지기와 함께 사용(ii) 유틸리티 함수:=================== ============================ ===================================================================================================================================================== 유형 이름 기능 =================== ============================ ===================================================================================================================================================== 데이터 generate_data 합성 데이터 생성; 다변량 가우시안에서 추출한 정상 데이터, 균등 분포에서 추출한 이상치 데이터 generate_data_clusters 더 복잡한 패턴을 위한 클러스터 형태의 합성 데이터 생성 평가 evaluate_print 탐지기에 대한 ROC-AUC 및 Precision @ Rank n 출력 평가 precision_n_scores Precision @ Rank n 계산 유틸리티 get_label_n 상위 n개 점수에 1을 할당하여 원시 이상치 점수를 이진 레이블로 변환 통계 wpearsonr 두 표본의 가중치 적용 Pearson 상관 계수 계산 인코딩 resolve_encoder 문자열 이름, BaseEncoder 인스턴스 또는 콜러블에서 인코더 확인 인코딩 SentenceTransformerEncoder sentence-transformers 모델(예: MiniLM, mpnet)을 통해 텍스트 인코딩 인코딩 OpenAIEncoder OpenAI Embeddings API(text-embedding-3-small/large)를 통해 텍스트 인코딩 인코딩 HuggingFaceEncoder HuggingFace transformers(BERT, DINOv2, CLIP)를 통해 텍스트 또는 이미지 인코딩 =================== ============================ =====================================================================================================================================================
이상치 탐지 빠른 시작 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PyOD는 머신러닝 커뮤니티에서 여러 특집 게시물과 튜토리얼을 통해 널리 인정받아 왔습니다.
Analytics Vidhya: PyOD 라이브러리를 사용하여 Python에서 이상치 탐지를 배우는 멋진 튜토리얼 <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>__
KDnuggets: 이상치 탐지 방법의 직관적인 시각화 <https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html>, PyOD의 이상치 탐지 방법 개요 <https://www.kdnuggets.com/2019/06/overview-outlier-detection-methods-pyod.html>
Towards Data Science: 초보자를 위한 이상 탐지 <https://towardsdatascience.com/anomaly-detection-for-dummies-15f148e559c1>__
"examples/knn_example.py" <https://github.com/yzhao062/pyod/blob/master/examples/knn_example.py>__
는 kNN 탐지기 사용의 기본 API를 보여줍니다. 다른 모든 알고리즘의 API도 일관되거나 유사하다는 점에 유의하세요.
실행 예제에 대한 더 자세한 지침은 examples 디렉토리 <https://github.com/yzhao062/pyod/blob/master/examples>_에서 확인할 수 있습니다.
#. kNN 탐지기를 초기화하고, 모델을 피팅하고, 예측을 수행합니다.
.. code-block:: python
from pyod.models.knn import KNN # kNN detector
from pyod.utils.data import generate_data
contamination = 0.1 # percentage of outliers
n_train = 200 # number of training points
n_test = 100 # number of testing points
# generate sample data
X_train, X_test, y_train, y_test = generate_data(
n_train=n_train, n_test=n_test, n_features=2,
contamination=contamination, random_state=42)
# train kNN detector
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
# get the prediction label and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# it is possible to get the prediction confidence as well
y_test_pred, y_test_pred_confidence = clf.predict(X_test, return_confidence=True) # outlier labels (0 or 1) and confidence in the range of [0,1]
#. ROC와 Precision @ Rank n(p@n)으로 예측을 평가합니다.
.. code-block:: python
from pyod.utils.data import evaluate_print
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
#. 샘플 출력 및 시각화를 확인합니다.
.. code-block:: python
On Training Data:
KNN ROC:0.9992, precision @ rank n:0.95
On Test Data:
KNN ROC:1.0, precision @ rank n:1.0
.. code-block:: python
from pyod.utils.example import visualize
visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
y_test_pred, show_figure=True, save_figure=False)
감사의 말 ^^^^^^^^^^^^^^^
이 자료는 미국 국립과학재단(National Science Foundation)의 지원을 받아 수행된 연구 결과입니다.
과제 번호 2346158 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346158>_
"NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for
Anomaly Detection." 프로젝트에 대한 지원입니다. 본 과제는 University of Illinois Chicago를
주관 기관으로 하며, Illinois Institute of Technology, Lehigh University,
University of Southern California가 하위 수혜 기관으로 등재되어 있습니다.
이 자료에 표현된 모든 의견, 발견, 결론 또는 권고 사항은 저자(들)의 것이며, 미국 국립과학재단(National Science Foundation)의 견해를 반드시 반영하는 것은 아닙니다.
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