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Feature Engineering for Machine Learning - Alice Zheng

特征工程

机器学习

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Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists Alice Zheng and Amanda Casari

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Alice Zheng & Amanda Casari

Feature

Engineering

for Machine Learning

PRINCIPLES AND TECHNIQUES FOR DATA SCIENTISTS

Alice Zheng and Amanda Casari

Feature Engineering for

Machine Learning

Principles and Techniques for Data Scientists

Boston Farnham Sebastopol

Tokyo

Beijing Boston Farnham Sebastopol

Tokyo

Beijing

978-1-491-95324-2

[LSI]

Feature Engineering for Machine Learning

by Alice Zheng and Amanda Casari

Printed in the United States of America.

Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are

also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐

tutional sales department: 800-998-9938 or corporate@oreilly.com.

Editors: Rachel Roumeliotis and Jeff Bleiel Indexer: Ellen Troutman

Production Editor: Kristen Brown Interior Designer: David Futato

Copyeditor: Rachel Head Cover Designer: Karen Montgomery

Proofreader: Sonia Saruba Illustrator: Rebecca Demarest

April 2018: First Edition

Revision History for the First Edition

2018-03-23: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491953242 for release details.

The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Feature Engineering for Machine

Learning, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc.

While the publisher and the authors have used good faith efforts to ensure that the information and

instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility

for errors or omissions, including without limitation responsibility for damages resulting from the use of

or reliance on this work. Use of the information and instructions contained in this work is at your own

risk. If any code samples or other technology this work contains or describes is subject to open source

licenses or the intellectual property rights of others, it is your responsibility to ensure that your use

thereof complies with such licenses and/or rights.

Table of Contents

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

The Machine Learning Pipeline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Data 1

Tasks 1

Models 2

Features 3

Model Evaluation 3

Fancy Tricks with Simple Numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Scalars, Vectors, and Spaces 6

Dealing with Counts 8

Binarization 9

Quantization or Binning 10

Log Transformation 15

Log Transform in Action 19

Power Transforms: Generalization of the Log Transform 23

Feature Scaling or Normalization 29

Min-Max Scaling 30

Standardization (Variance Scaling) 31

ℓ

Normalization 32

Interaction Features 35

Feature Selection 38

Summary 39

Bibliography 39

Text Data: Flattening, Filtering, and Chunking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Bag-of-X: Turning Natural Text into Flat Vectors 42

iii

剩余216页未读，继续阅读

请将以下内容进行压缩凝练（注意：可对内容进行删减，对错误进行修正，对语句顺序进行调整，符合美式英语标准，符合英语母语者语言习惯，句子简明易懂，术语使用准确，保留文章结构、不偏离论文主要内容）： Traditional machine learning methods can be divided into two categories: supervised and unsupervised learning methods. The supervised learning methods include Support Vector Machine (SVM), Random Forest, Decision Tree, etc. The unsupervised learning methods include Principal Component Analysis (PCA), Independent Component Analysis (ICA), K-means clustering, Non-Negative Matrix Decomposition (NMF), etc. Traditional machine learning methods also have shortcomings, which require high data quality, professional processing and feature engineering of data by experts according to experience and expertise, and high labor cost. At the same time, the use of traditional machine learning methods have its limitation because they need to be combined with the geological situation of a certain region to target anomaly detection, and cannot be universally applied to other regions.

Traditional machine learning can be divided into supervised and unsupervised methods, such as Support Vector Machines, Random Forests, Decision Trees, Principal Component Analysis, Independent Compo

AutoInt，即Automatic Feature Interaction Learning via Self-Attentive Neural Networks，请用继承keras.Layer的方式实现，并重写Layer的init、build、call、get_config方法

AutoInt 是一种通过自注意力神经网络来学习特征交互的方法。可以通过继承 keras.layers.Layer 类来实现 AutoInt。重写 init、build、call、get_config 方法可以自定义层的初始化、构建、运行和序列化。以下是一个简单的示例代码： ``` from keras import layers class AutoInt(layers.Layer):

core-js@2.6.12: core-js@<3.4 is no longer maintained and not recommended for usage due to the number of issues. because of the v8 engine whims, feature detection in old core-js versions could cause a slowdown up to 100x even if nothing is polyfilled. please, upgrade your dependencies to the actual version of core-js.

b'core-js@2.6.12: 由于遇到许多问题，不再维护 core-js@<3.4，并且不建议使用。由于v8引擎的变化，旧版本 core-js 特性检测可能会导致速度减慢100倍，即使没有使用任何polyfill。请升级您的依赖到实际版本的 core-js。'

npm warn deprecated core-js@2.6.12: core-js@<3.4 is no longer maintained and not recommended for usage due to the number of issues. because of the v8 engine whims, feature detection in old core-js versions could cause a slowdown up to 100x even if nothing is polyfilled. please, upgrade your dependencies to the actual version of core-js.

### 回答1：该警告表示 core-js@2.6.12 已过时，并且由于存在大量问题，不建议使用 core-js@<3.4。在旧版本的 core-js 中进行特性检测可能会导致由于 v8 引擎的变化而导致的性能下降甚至多达 100 倍，即使没有进行任何 polyfill。因此，请升级您的依赖项至实际版本的 core-js。 ### 回答2：该警告提示核心模块 core-js 的版本已

metric learning via feature weighting for scalable image retrieval

本文介绍了一种基于特征权重度量学习的方法，可以应用于可扩展图像检索。该方法利用多任务学习，将特征选择和度量学习相结合，从而使得检索性能更加稳定和有效。具体地，通过对训练数据进行聚类并从中选择代表性的点，然后基于这些点来计算特征的相对重要性并进行调整，最终实现度量学习的优化。与传统的方法相比，本文的方法具有更好的可扩展性和鲁棒性，能够应用于大规模图像检索任务。本文的实验结果表明，在多个图像检索数据集

npm warn deprecated core-js@2.6.12: core-js@<3.23.3 is no longer maintained and not recommended for usage due to the number of issues. because of the v8 engine whims, feature detection in old core-js versions could cause a slowdown up to 100x even if nothing is polyfilled. some versions have web compatibility issues. please, upgrade your dependencies to the actual version of core-js.

### 回答1： npm警告已弃用core-js@2.6.12：由于存在大量问题，不再维护core-js@<3.23.3，不建议使用。由于v8引擎的任性，旧版core-js中的特性检测可能会导致减速高达100倍，即使没有进行任何填充。某些版本存在Web兼容性问题。请升级您的依赖项到实际版本的core-js。

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