피처 엔지니어링,feature-engineering,에 관한 사이트 & 코드code들

Machine Learning/resources 2022. 2. 18. 18:09
* 아래는 라이브러리 이용해서 피처 늘리는 예시 코드 및 설명
 
 
PCA를 이용한 Feature Extraction 설명 및 코드

PCA를 이용한 Feature Extraction 설명 및 코드
 

 

https://medium.com/analytics-vidhya/feature-engineering-using-featuretools-with-code-10f8c83e5f68 (https://github.com/ranasingh-gkp/Feature_engineering_Featuretools)

 

https://www.analyticsvidhya.com/blog/2018/08/guide-automated-feature-engineering-featuretools-python/

 

https://data-newbie.tistory.com/815 은 원본코드 https://www.kaggle.com/frednavruzov/auto-feature-generation-featuretools-example 를 버전 업데이트에 맞춰 수정한 최신 코드이다.

 

Featuretools의 공식 홈페이지의 예제코드

https://featuretools.alteryx.com/en/stable/ , https://featuretools.alteryx.com/en/stable/getting_started/using_entitysets.html

 

https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/

https://youtube.com/playlist?list=PLSlDi2AkDv832sRHreZKoyyzJnGxLkcfF

Feature Tools에 대한 설문 문서 페이지들: https://primitives.featurelabs.com/ , https://docs.featuretools.com/en/stable/api_reference.html#feature-primitives

* 아래는 PCA를 이용한 Feature Extraction 설명 및 코드

https://towardsdatascience.com/feature-extraction-using-principal-component-analysis-a-simplified-visual-demo-e5592ced100a

https://medium.com/@mayureshrpalav/principal-component-analysis-feature-extraction-technique-3f480d7b9697

https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/140579#140579

https://vitalflux.com/feature-extraction-pca-python-example/

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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