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machine-learning-interpretability

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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

  • Updated Jun 17, 2024
  • Jupyter Notebook
explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu

  • Updated Aug 21, 2024
  • Jupyter Notebook

This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".

  • Updated Jul 13, 2022
  • Python

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