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The art of feature engineering : essentials for machine learning / First edition

The art of feature engineering : essentials for machine learning / First edition (1회 대출)

자료유형
단행본
개인저자
Duboue, Pablo, 1976- author.
서명 / 저자사항
The art of feature engineering : essentials for machine learning / Pablo Duboue, Textualization Software Ltd.
판사항
First edition.
발행사항
Cambridge ;   New York, NY :   Cambridge University Press,   2020.  
형태사항
xii, 274 p. : ill. ; 23 cm.
ISBN
9781108709385 (paperback)
요약
"When working with a data set, a machine learning engineer might train a model but find that the results are not as good as they need. To get better results, they can try to improve the model or collect more data, but there is another avenue: feature engineering. The feature engineering process can help improve results by modifying the data's features to better capture the nature of the problem. This process is partly an art and partly a palette of tricks and recipes. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks"--
서지주기
Includes bibliographical references and index.
일반주제명
Machine learning. Python (Computer program language).
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008 201218s2020 enka b 001 0 eng d
010 ▼a 2019060140
020 ▼a 9781108709385 (paperback)
020 ▼z 9781108671682 (epub)
035 ▼a (KERIS)REF000019276351
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
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082 0 0 ▼a 006.3/1 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b D818a
100 1 ▼a Duboue, Pablo, ▼d 1976- ▼e author.
245 1 4 ▼a The art of feature engineering : ▼b essentials for machine learning / ▼c Pablo Duboue, Textualization Software Ltd.
250 ▼a First edition.
260 ▼a Cambridge ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 2020.
263 ▼a 2003
300 ▼a xii, 274 p. : ▼b ill. ; ▼c 23 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
504 ▼a Includes bibliographical references and index.
520 ▼a "When working with a data set, a machine learning engineer might train a model but find that the results are not as good as they need. To get better results, they can try to improve the model or collect more data, but there is another avenue: feature engineering. The feature engineering process can help improve results by modifying the data's features to better capture the nature of the problem. This process is partly an art and partly a palette of tricks and recipes. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks"-- ▼c Provided by publisher.
650 0 ▼a Machine learning.
650 0 ▼a Python (Computer program language).
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 D818a 등록번호 121255803 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Part I. Fundamentals: 1. Introduction; 2. Features, combined; 3. Features, expanded; 4. Features, reduced; 5. Advanced topics; Part II. Case Studies: 6. Graph data; 7. Timestamped data; 8. Textual data; 9. Image data; 10. Other domains.

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