ML Anthology

Using Multiple Models to Understand Data

Kayur Patel, Steven Mark Drucker, James Fogarty, Ashish Kapoor, Desney S. Tan
IJCAI 2011 pp. 1723-1728
doi:10.5591/978-1-57735-516-8/IJCAI11-289 /ijcai/2011/patel2011ijcai-using/

Abstract

A human’s ability to diagnose errors, gather data, and generate features in order to build better models is largely untapped. We hypothesize that analyzing results from multiple models can help people diagnose errors by understanding relationships among data, features, and algorithms. These relationships might otherwise be masked by the bias inherent to any individual model. We demonstrate this approach in our Prospect system, show how multiple models can be used to detect label noise and aid in generating new features, and validate our methods in a pair of experiments.

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Cite

Text

Patel et al. "Using Multiple Models to Understand Data." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-289

Markdown

[Patel et al. "Using Multiple Models to Understand Data." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/patel2011ijcai-using/) doi:10.5591/978-1-57735-516-8/IJCAI11-289

BibTeX

@inproceedings{patel2011ijcai-using,
  title     = {{Using Multiple Models to Understand Data}},
  author    = {Patel, Kayur and Drucker, Steven Mark and Fogarty, James and Kapoor, Ashish and Tan, Desney S.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {1723-1728},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-289},
  url       = {https://mlanthology.org/ijcai/2011/patel2011ijcai-using/}
}