The art of feature engineering : essentials for machine learning / Pablo Duboue.

Format:

Book

Author/Creator:

Duboue, Pablo, 1976- author.

Contributor:

Engineering Book Fund.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

xii, 273 pages : illustrations ; 23 cm

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2020.

Summary:

"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"-- Provided by publisher.

Contents:

Machine generated contents note: pt. ONE FUNDAMENTALS

1. Introduction

1.1. Feature Engineering

1.2. Evaluation

1.3. Cycles

1.4. Analysis

1.5. Other Processes

1.6. Discussion

1.7. Learning More

2. Features, Combined: Normalization, Discretization and Outliers

2.1. Normalizing Features

2.2. Discretization and Binning

2.3. Descriptive Features

2.4. Dealing with Outliers

2.5. Advanced Techniques

2.6. Learning More

3. Features, Expanded: Computable Features, Imputation and Kernels

3.1. Computable Features

3.2. Imputation

3.3. Decomposing Complex Features

3.4. Kernel-Induced Feature Expansion

3.5. Learning More

4. Features, Reduced: Feature Selection, Dimensionality Reduction and Embeddings

4.1. Feature Selection

4.2. Regularization and Embedded Feature Selection

4.3. Dimensionality Reduction

4.4. Learning More

5. Advanced Topics: Variable-Length Data and Automated Feature Engineering

5.1. Variable-Length Feature Vectors

5.2. Instance-Based Engineering

5.3. Deep Learning and Feature Engineering

5.4. Automated Feature Engineering

5.5. Learning More

pt. TWO CASE STUDIES

6. Graph Data

6.1. WikiCities Dataset

6.2. Exploratory Data Analysis (EDA)

6.3. First Feature Set

6.4. Second Feature Set

6.5. Final Feature Sets

6.6. Learning More

7. Timestamped Data

7.1. WikiCities: Historical Features

7.2. Time Lagged Features

7.3. Sliding Windows

7.4. Third Featurization: EMA

7.5. Historical Data as Data Expansion

7.6. Time Series

7.7. Learning More

8. Textual Data

8.1. WikiCities: Text

8.2. Exploratory Data Analysis

8.3. Numeric Tokens Only

8.4. Bag-of-Words

8.5. Stop Words and Morphological Features

8.6. Features in Context

8.7. Skip Bigrams and Feature Hashing

8.8. Dimensionality Reduction and Embeddings

8.9. Closing Remarks

8.10. Learning More

9. Image Data

9.1. WikiCities: Satellite Images

9.2. Exploratory Data Analysis

9.3. Pixels as Features

9.4. Automatic Dataset Expansion

9.5. Descriptive Features: Histograms

9.6. Local Feature Detectors: Corners

9.7. Dimensionality Reduction: HOGs

9.8. Closing Remarks

9.9. Learning More

10. Other Domains: Video, GIS and Preferences

10.1. Video

10.2. Geographical Features

10.3. Preferences.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Engineering Book Fund.

Other Format:

Online version: Duboue, Pablo, 1976- The art of feature engineering

ISBN:

9781108709385

1108709389

OCLC:

1112139772

Publisher Number:

99987451201