Responsible Machine Learning with Error Analysis

 By Medium - 2021-03-05

Description

Machine Learning (ML) teams who deploy models in the real world often face the challenges of conducting rigorous performance evaluation and testing for ML models. How often do we read claims such as…

Summary

  • Learn how to identify and diagnose errors in Machine Learning Overview Machine Learning (ML) teams who deploy models in the real world often face the challenges of conducting rigorous performance evaluation and testing for ML models.
  • At the same time, there may exist several dimensions of the input feature space that a practitioner may be interested in taking a deep dive and ask questions such as “What happens to the accuracy of the recognition model in a self-driving car when it is dark and snowing outside?” Image by authors) Cohort definition and manipulation To specialize the analysis and allow for deep dives, both error identification views can be generated for any data cohort and not only for the whole benchmark.
  • Users can explore dataset statistics and distributions by selecting different features and estimators along the two axes of the data explorer.

 

Topics

  1. Backend (0.28)
  2. Machine_Learning (0.16)
  3. NLP (0.15)

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