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2275 results in Pattern Recognition and Machine Learning

Page 59 of 114

  • 10 - Other Domains: Video, GIS and Preferences

  • from Part II - Case Studies
  • Pablo Duboue
  • Book:
    The Art of Feature Engineering
    Published online:
    29 May 2020
    Print publication:
    25 June 2020, pp 233-245

  • Summary

    This chapter showcases three domains different from the other domains to highlight particular techniques and problems missing from the previous chapters. Each domain uses a small dataset assembled just for the case study and undergoes only one featurization process. The first domain is video domain, where a mouse pointer tracking problem on a video is studied to showcase two key issues in feature engineering for video: speed of processing and reusability of results from a previous feature extraction. The second domain is geographical information systems, where bird migration data is studied to showcase the value of key points as physical summaries of paths and bi-dimensional clustering of points using R-trees. Finally, preference data is studied via movie preferences to showcase large scale imputation of missing features.

The Art of Feature Engineering
  • Essentials for Machine Learning
  • Pablo Duboue
  • Published online:
    29 May 2020
    Print publication:
    25 June 2020
  • 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.

Page 59 of 114