by Pablo Duboue, PhD
Improve your machine learning results with a process that is both art and engineering. Express real problems as inputs to machine learning algorithms to better solve them.
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. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data.
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.