The Art of Feature Engineering
When machine learning engineers work with
data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data,
they can use the feature engineering process to help improve results by modifying the data's features to better capture the
nature of the problem. 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, 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.
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.
A practical guide for data scientists who want to improve the performance of any machine learning
solution with feature engineering.