Feature engineering for machine learning : principles and techniques for data scientists / Alice Zheng and Amanda Casari.

Format:

Book

Author/Creator:

Zheng, Alice, author.

Casari, Amanda, author.

Language:

English

Subjects (All):

Machine learning.

Data mining.

Physical Description:

1 online resource (xiii, 200 pages) : illustrations

Edition:

First edition.

Place of Publication:

Beijing : O'Reilly, [2018]

System Details:

text file

Summary:

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

Contents:

The machine learning pipeline

Fancy tricks with simple numbers

Text data : flattening, filtering, and chunking

The effects of feature scaling : from bag-of-words to Tf-Idf

Categorical variables : counting eggs in the age of robotic chickens

Dimensionality reduction : squashing the data pancake with PCA

Nonlinear featurization via K-means model stacking

Automating the featurizer : image feature extraction and deep learning

Back to the feature : building an academic paper recommender

Linear modeling and linear algebra basics.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781491953198

1491953195

9781491953235

1491953233

9781491953211

1491953217

OCLC:

1089811256