| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250422b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781491953242 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
ZHE |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Zheng, Alice |
| 245 ## - TITLE STATEMENT |
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| Title |
Feature engineering for machine learning : principles and techniques for data scientists |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
O'Reilly, |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Beijing : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 200 p. ; |
| Other physical details |
ill. (some col.), |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
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| Price amount |
900.00 |
| Price type code |
₹ |
| Unit of pricing |
01 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
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| Topical term or geographic name as entry element |
Box-Cox transform |
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| Topical term or geographic name as entry element |
Categorical variable |
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| Topical term or geographic name as entry element |
Collaborative Filtering |
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| Topical term or geographic name as entry element |
Data points |
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| Topical term or geographic name as entry element |
Dummy coding |
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| Topical term or geographic name as entry element |
Feature space |
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| Topical term or geographic name as entry element |
Image gradients |
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| Topical term or geographic name as entry element |
Logistic Regression |
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| Topical term or geographic name as entry element |
Singular Value Decomposition |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Casari, Amanda |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Item type |
Books |